This disclosure relates generally to data analytics models, and
more particularly to method and system for adjusting and modifying
parameters to generate data analytics model.
Background
[002] Typically, a data analytics model has multiple varying stages
ranging from data ingestion to data pre-processing, feature selection, and
model training. In each of these stages a user is provided with multiple
choices that may be applied to curate data to be made available to a next
consecutive stage. Prevalent data analytics training mechanisms leave
choice of selecting the options for the pre-processing stage and the feature
selection stage to the user, and based on the options selected by the user,
the data is transformed and made available for training. In addition, in the
model training stage, an algorithm selection and scoring criteria to be
evaluated is also left to the user.
[003] Though the user may personally specify customized options for
individual problems, the mechanism suffers from drawbacks such as nonevaluation of all possible available options, and the non-utilized option to be
one of an optimized parameters to be used to solve a problem. Therefore,
there is a need in the art for improved methods and systems for
recommending tuning of parameters to generate the data analytics model.
SUMMARY
[004] In an embodiment, a method for recommending tuning of
parameters to generate a data analytics model is disclosed. In one example,
the method may include identifying at a data pre-processing stage, for each
of a plurality of pre-processing operations, a pre-processing subset from an
associated set of predefined pre-processing methods for a predefined
objective by a recommender device. Each pre-processing subset may include
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a list of ranked predefined pre-processing methods. The method may further
include identifying at a feature selection stage, for each of a plurality of feature
selection operations, a feature subset from an associated set of predefined
feature selection methods for the predefined objective. Each feature subset
may include a list of ranked predefined feature selection methods. The
method may further include identifying at a model training stage, for each of
a plurality of model training operations, a training subset from an associated
set of predefined model training methods for the predefined objective. Each
training subset may include a list of ranked predefined model training
methods. The method may further include generating a plurality of data
analytics tuples. Each of the plurality of data analytics tuples may include a
predefined pre-processing method selected from the associated preprocessing subset, a predefined feature selection method selected from the
associated feature subset, and a predefined model training method selected
from the associated training subset. The method may further include selecting
a data analytics tuple from the plurality of data analytics tuples. An output
result of the data analytics tuple may include highest ranked results for the
predefined objective, and the data analytics tuple corresponds to the data
analytics model.
[005] In another embodiment, a system for recommending tuning of
parameters to generate a data analytics model is disclosed. In one example,
the system may include a recommender device comprising a processor and
a memory communicatively coupled to the processor, wherein the memory
stores processor-executable instructions, which, on execution, may cause
the processor to identify at a data pre-processing stage for each of a plurality
of pre-processing operations, a pre-processing subset from an associated set
of predefined pre-processing methods for a predefined objective. Each preprocessing subset may include a list of ranked predefined pre-processing
methods. The processor-executable instructions, on execution, may further
cause the processor to identify at a feature selection stage for each of a
plurality of feature selection operations, a feature subset from an associated
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set of predefined feature selection methods for the predefined objective. Each
feature subset may include a list of ranked predefined feature selection
methods. The processor-executable instructions, on execution, may further
cause the processor to identify at a model training stage for each of a plurality
of model training operations, a training subset from an associated set of
predefined model training methods for the predefined objective. Each training
subset may include a list of ranked predefined model training methods. The
processor-executable instructions, on execution, may further cause the
processor to generate a plurality of data analytics tuples. Each of the plurality
of data analytics tuples may include a predefined pre-processing method
selected from the associated pre-processing subset, a predefined feature
selection method selected from the associated feature subset, and a
predefined model training method selected from the associated training
subset. The processor-executable instructions, on execution, may further
cause the processor to select a data analytics tuple from the plurality of data
analytics tuples. An output result of the data analytics tuple may include
highest ranked results for the predefined objective, and the data analytics
tuple may correspond to the data analytics model.
[006] It is to be understood that both the foregoing general description
and the following detailed description are exemplary and explanatory only
and are not restrictive of the invention, as claimed.
BRIEF DESCRIPTION OF THE DRAWINGS
[007] The accompanying drawings, which are incorporated in and
constitute a part of this disclosure, illustrate exemplary embodiments and,
together with the description, serve to explain the disclosed principles.
[008] FIG. 1 is a block diagram of an exemplary system for
recommending tuning of parameters to generate a data analytics model, in
accordance with some embodiments.
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[009] FIG. 2 illustrates a functional block diagram of a recommender
device implemented by the exemplary system of FIG. 1, in accordance with
some embodiments.
[010] FIGS. 3A and 3B, illustrate an exemplary process for
recommending tuning of parameters to generate a data analytics model, in
accordance with some embodiments.
[011] FIG. 4 illustrates an exemplary functioning of the recommender
device of the system, at a data pre-processing stage, in accordance with
some embodiments.
[012] FIG. 5 illustrates an exemplary functioning of the recommender
device of the system, at a feature selection stage, in accordance with some
embodiments.
[013] FIG. 6 illustrates an exemplary functioning of the recommender
device of the system, at a model training stage, in accordance with some
embodiments.
[014] FIG. 7 is an exemplary representation of a transformation
options pipeline, in accordance with some embodiments.
[015] FIG. 8 is an exemplary representation of a pipeline tuning
matrix, in accordance with some embodiments.
[016] FIG. 9 is a block diagram of an exemplary computer system for
implementing various embodiments.
DETAILED DESCRIPTION
[017] Exemplary embodiments are described with reference to the
accompanying drawings. Wherever convenient, the same reference numbers
are used throughout the drawings to refer to the same or like parts. While
examples and features of disclosed principles are described herein,
modifications, adaptations, and other implementations are possible without
departing from the spirit and scope of the disclosed embodiments. It is
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intended that the following detailed description be considered as exemplary
only, with the true scope and spirit being indicated by the following claims.
Additional illustrative embodiments are listed below.
[018] Referring now to FIG. 1, an exemplary system 100 for
recommending tuning of parameters to generate a data analytics model is
illustrated, in accordance with some embodiments. The system 100 may
implement a recommender device 102 (for example, a server, a desktop, a
laptop, a notebook, a netbook, a tablet, a smartphone, a mobile phone, or
any other computing device), in accordance with some embodiments/ . The
recommender device 102 may recommend tuning of parameters (e.g., data
processing parameters, feature selection parameters, model training
parameters) to generate a data analytics model by identifying a preprocessing subset from an associated set of predefined pre-processing
methods, a feature subset from an associated set of predefined feature
selection methods, and a training subset from an associated set of predefined
model training methods.
[019] It should be noted that, in some embodiments, for identification
at the data pre-processing stage, the recommender device 102 may
generate, for each of a plurality of pre-processing operations, a preprocessing subset from an associated set of predefined pre-processing
methods, and may assign, for each of the plurality of pre-processing
operations, a rank to each predefined pre-processing method in an
associated pre-processing subset. The plurality of pre-processing operations
may include, but are not limited to at least one of an impute operation, an
outlier detection operation, an outlier treatment operation, a rescale
operation, and a transform operation. Further, for identification at the feature
selection stage, the recommender device 102 may generate for each of a
plurality of feature selection operations, a feature subset from an associated
set of predefined feature selection methods, and may assign, for each of the
plurality of feature selection operations, a rank to each predefined feature
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selection method in an associated feature subset. The plurality of feature
selection operations may include, but are not limited to at least one of a
correlation operation, a model-based operation, and a feature reduction
operation. Additionally, for identification at the model training stage, the
recommender device 102 may generate for each of the plurality of model
training operations, a training subset from an associated set of predefined
model training methods, and assign, for each of the plurality of model training
operations, a rank to each predefined model training method in an associated
training subset. The plurality of model training operations may include at least
one of, but are not limited to an algorithm selection operation, a
hyperparameters tuning operation, and a model optimization operation.
[020] As will be described in greater detail in conjunction with FIGS.
2 – 9, the recommender device 102 may transform an input data to generate
transformed data based on the predefined objective. The recommender
device 102 may transform the input data. The transformation may include
processing at the data pre-processing stage. The processing at this stage
may include processing, through a first plurality of recommendation layers,
the input data, multiple sets of predefined pre-processing methods, and a list
of problem types associated with the predefined objective, to generate a set
of pre-processed data. Further, each set in the plurality of sets of predefined
pre-processing methods may correspond to a pre-processing operation from
the plurality of pre-processing operations. Further, each of set of predefined
pre-processing methods, at the data pre-processing stage, may be ranked
based on a criteria appropriate for each of set of predefined pre-processing
methods. The first plurality of recommendation layers may include a predefined statistical and quantitative method layer, a pre-defined rules and
practices layer, and a predefined meta learning-based layer. Additionally, the
ranks is assigned after processing through the first plurality of
recommendation layers based on an associated criteria.
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[021] The recommender device 102 may transform the input data to
generate the transformed data. This may further include processing at the
feature selection stage, through a second plurality of recommendation layers,
the set of pre-processed data, the plurality of sets of predefined feature
selection methods, and the list of problem types, to generate a transformed
data. Each set in the plurality of sets of predefined feature selection methods
may correspond to a feature selection operation from a plurality of feature
selection operations. Further, at the feature selection stage, ranking of each
of the set of predefined feature selection methods is performed based on a
criteria appropriate for the each of set of predefined feature selection
methods. The second plurality of recommendation layers may include a predefined statistical and quantitative method layer, a pre-defined rules and
practices layer, and a predefined meta learning-based layer. Additionally, the
ranks are assigned after processing through the second plurality of
recommendation layers based on an associated criteria.
[022] Once the input data has been processed to generate the
transformed data, the recommender device 102 may process at a model
training stage, through a third plurality of recommendation layers, the
transformed data, the plurality of sets of predefined model training methods,
and the list of problem types. Each set in the multiple sets of predefined model
training methods may correspond to a model training operation from a
plurality of model training operations. Further, at the model training stage, the
third plurality of recommendation layers may include a pre-defined statistical
and quantitative method layer, a pre-defined rules and practices layer, and a
predefined meta learning-based layer. Additionally, the ranks are assigned
after processing through the third plurality of recommendation layers based
on an associated criteria.
[023] The system 100 may further include a display 108. The system
100 may interact with a user via a user interface 110 accessible via the
display 108. The system 100 may also include one or more external devices
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112. In some embodiments, the recommender device 102 may interact with
the one or more external devices 112 over a communication network 114 for
sending or receiving various data. The external devices 112 may include, but
may not be limited to, a remote server, a digital device, or another computing
system.
[024] Referring now to FIG. 2, a functional block diagram of a
recommender device 200 is illustrated, in accordance with some
embodiments. In an embodiment, the recommender device 200 may include
a data preprocessing module 202, a feature selecting module 204, a model
training module 206, a data analytics tuple generating module 208, and a
data analytics tuple selecting module 210. In such an embodiment, the
recommender device 200 may be analogous to the recommender device 102
of the system 100.
[025] For recommending tuning of parameters to generate a data
analytics model, an input data 212 may be transformed to generate a
transformed data based on a predefined objective. In an embodiment,
transformation of the input data 212 may include processing at a data preprocessing stage, through a first plurality of recommendation layers, the input
data, a plurality of sets of predefined pre-processing methods, and a list of
problem types associated with the predefined objective, to generate a set of
pre-processed data. Each set in the multiple sets of predefined preprocessing methods may correspond to a pre-processing operation from the
plurality of pre-processing operations.
[026] The data preprocessing module 202 may at the data preprocessing stage, identify, for each of the plurality of pre-processing
operations, a pre-processing subset from an associated set of predefined
pre-processing methods for the predefined objective. Each pre-processing
subset may include a list of ranked predefined pre-processing methods. As
an example, the pre-processing methods may include such as a missing
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values handling method, data outliers and out of range values handling
method, a data transformation and re-scaling method, and the like.
[027] The identification at the data pre-processing stage may include
generating for each of the plurality of pre-processing operations, the preprocessing subset from the associated set of predefined pre-processing
methods. This may be based on the result of processing through each of the
first plurality of recommendation layers. The first plurality of recommendation
layers may include a pre-defined statistical and quantitative method layer, a
pre-defined rules and practices layer, and a predefined meta learning-based
layer. It will be apparent to a person skilled in the art that the processing via
the first plurality of recommendation layers may be sequential or parallel. The
multiple pre-processing operations may include at least one of an impute
operation, an outlier detection operation, an outlier treatment operation, a
rescale operation, and a transform operation.
[028] Further, for each of the plurality of pre-processing operations, a
rank may be assigned to each predefined pre-processing method in the
associated pre-processing subset. Additionally, each of set of predefined preprocessing methods, at the data pre-processing stage, may be ranked. The
ranking may be performed based on a criteria appropriate for each of set of
predefined pre-processing methods. As discussed before, the ranking may
be based on processing via the first plurality of recommendation layers.
[029] For each of the plurality of feature selection operations, the
feature selecting module 204, at a feature selection stage, may identify a
feature subset from an associated set of predefined feature selection
methods for the predefined objective. Each feature subset may include a list
of ranked predefined feature selection methods. As an example, the
predefined feature selection methods may include such as a high cardinality
and low variance features handling method, a high dimensionality of features
handling method, noisy features handling method, a number of features
reducing method, and the like.
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[030] Further, transforming the input data 212 to generate the
transformed data may include processing, at the feature selection stage,
through a second plurality of recommendation layers, the set of preprocessed data, the plurality of sets of predefined feature selection methods,
and the list of problem types. Each set in the plurality of sets of predefined
feature selection methods may correspond to a feature selection operation
from a plurality of feature selection operations. The plurality of feature
selection operations may include, but are not limited to at least one of a
correlation operation, a model-based operation, and a feature reduction
operation.
[031] In an embodiment, identifying at the feature selection stage
may include generating for each of the multiple feature selection operations,
the feature subset from the associated set of predefined feature selection
methods. The feature subset may be generated based on the result of
processing through each of the second plurality of recommendation layers.
Further, for each of the plurality of feature selection operations, a rank may
be assigned to each predefined feature selection method in the associated
feature subset. The ranking of each predefined feature selection method, at
the feature selection stage, may be performed based on a criteria appropriate
for each of the set of predefined feature selection methods. The second
plurality of recommendation layers may include a pre-defined statistical and
quantitative method layer, a pre-defined rules and practices layer, and a
predefined meta learning-based layer..
[032] The model training module 206, may identify, for each of
multiple model training operations, a training subset from an associated set
of predefined model training methods for the predefined objective. Each
training subset may include a list of ranked predefined model training
methods. Further, processing at a model training stage is done through a third
plurality of recommendation layers based on the transformed data, the
plurality of sets of predefined model training methods, and the list of problem
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types. Each set in the plurality of sets of predefined model training methods
may correspond to a model training operation from multiple model training
operations. The plurality of model training operations may include at least one
of an algorithm selection operation, a hyperparameters tuning operation, and
a model optimization operation.
[033] As an example, the predefined model training methods may
include such as a varied class of problems handling method, multiple models
(ML/DL) handling method, optimizing model parameters method, required
scoring criteria fitting method, and the like. In an embodiment, identification
at the model training stage may include generating for each of the plurality of
model training operations, the training subset from the associated set of
predefined model training methods, based on the result of processing through
each of the third plurality of recommendation layers. Further, for each of the
plurality of model training operations, a rank may be assigned to each
predefined model training method in the associated training subset.
Additionally, at the model training stage, the ranking of the each predefined
model training method may be performed based on a criteria appropriate for
the each of set of predefined model training methods. The third plurality of
recommendation layers may include a pre-defined statistical and quantitative
method layer, a pre-defined rules and practices layer, and a predefined meta
learning-based layer..
[034] The data analytics tuple generating module 208, may generate
a plurality of data analytics tuples. Each of the plurality of data analytics tuples
may include a predefined pre-processing method selected from the
associated pre-processing subset, a predefined feature selection method
selected from the associated feature subset, and a predefined model training
method selected from the associated training subset. In an exemplary
embodiment, the data analytics tuple generating module 208 may facilitate to
generate a set of transformed data. This may be done by listing down and
exposing all possible data vectors that may participate in a model training
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process. Further, the plurality of data analytics tuples may be depicted as a
matrix of possible combinations, for example, data preprocessing options,
feature selection options, and the like.
[035] The data analytics tuple selecting module 210 may select a data
analytics tuple from the plurality of data analytics tuples. An output result of
the data analytics tuple may include highest ranked results for the predefined
objective. Further, the data analytics tuple may correspond to the data
analytics model.
[036] It should be noted that all such aforementioned modules 202 –
210 may be represented as a single module or a combination of different
modules. Further, as will be appreciated by those skilled in the art, each of
the modules 202 – 210 may reside, in whole or in parts, on one device or
multiple devices in communication with each other. In some embodiments,
each of the modules 202 – 210 may be implemented as dedicated hardware
circuit comprising custom application-specific integrated circuit (ASIC) or gate
arrays, off-the-shelf semiconductors such as logic chips, transistors, or other
discrete components. Each of the modules 202 – 210 may also be
implemented in a programmable hardware device such as a field
programmable gate array (FPGA), programmable array logic, programmable
logic device, and so forth. Alternatively, each of the modules 202 – 210 may
be implemented in software for execution by various types of processors
(e.g., processor 104). An identified module of executable code may, for
instance, include one or more physical or logical blocks of computer
instructions, which may, for instance, be organized as an object, procedure,
function, or other construct. Nevertheless, the executables of an identified
module or component need not be physically located together, but may
include disparate instructions stored in different locations which, when joined
logically together, include the module and achieve the stated purpose of the
module. Indeed, a module of executable code could be a single instruction,
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or many instructions, and may even be distributed over several different code
segments, among different applications, and across several memory devices.
[037] As will be appreciated by one skilled in the art, a variety of
processes may be employed for identifying common requirements from
applications. For example, the exemplary system 100 and the associated the
recommender device 102 may identify common requirements from
applications by the processes discussed herein. In particular, as will be
appreciated by those of ordinary skill in the art, control logic and/or automated
routines for performing the techniques and steps described herein may be
implemented by the system 100 and the associated the recommender device
102 either by hardware, software, or combinations of hardware and software.
For example, suitable code may be accessed and executed by the one or
more processors on the system 100 to perform some or all of the techniques
described herein. Similarly, application specific integrated circuits (ASICs)
configured to perform some or all of the processes described herein may be
included in the one or more processors on the system 100.
[038] Referring now to FIGS. 3A and 3B, an exemplary process 300
for recommending tuning of parameters to generate a data analytics model
is depicted via a flowchart, in accordance with some embodiments. The
process 300 may be implemented by the recommender device 102 of the
system 100. The process 300 may include transforming an input data to
generate transformed data based on a predefined objective at step 302. As
may be appreciated, transforming the input data may include processing at
the data pre-processing stage, through a first plurality of recommendation
layers, the input data, multiple sets of predefined pre-processing methods,
and a list of problem types associated with the predefined objective. The
transformation of the input data is done to generate a set of pre-processed
data. Each set in the multiple sets of predefined pre-processing methods may
correspond to a pre-processing operation from the multiple pre-processing
operations.
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[039] In an embodiment, transforming the input data to generate the
transformed data further includes processing at the feature selection stage,
through a second plurality of recommendation layers, the set of preprocessed data, the plurality of sets of predefined feature selection methods,
and the list of problem types, to generate a transformed data. It may be noted
that each set in the multiple sets of predefined feature selection methods may
correspond to a feature selection operation from multiple feature selection
operations. Each of the first plurality and second plurality of recommendation
layers may include a pre-defined statistical and quantitative method layer, a
pre-defined rules and practices layer, and a predefined meta learning-based
layer. The process 300 may further include identifying, at a data preprocessing stage, for each of a plurality of pre-processing operations, a preprocessing subset from an associated set of predefined pre-processing
methods for a predefined objective, at step 304. It may be noted that each
pre-processing subset may include a list of ranked predefined pre-processing
methods. In an exemplary embodiment, the plurality of pre-processing
operations may include at least one of an impute operation, an outlier
detection operation, an outlier treatment operation, a rescale operation, and
a transform operation. In some embodiments, the identification at the data
pre-processing stage may include generating for each of the multiple preprocessing operations, the pre-processing subset from the associated set of
predefined pre-processing methods. The pre-processing subset may be
generated based on the result of processing through each of the first plurality
of recommendation layers, at step 306. Additionally, for each of the plurality
of pre-processing operations, a rank may be assigned to each predefined
pre-processing method in the associated pre-processing subset, at step 308,
based on processing via the first plurality of recommendation layers.
[040] Further, the process 300 may include identifying at a feature
selection stage, for each of a plurality of feature selection operations, a
feature subset from an associated set of predefined feature selection
methods for the predefined objective at step 310. Each feature subset may
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include a list of ranked predefined feature selection methods. In an exemplary
embodiment, the plurality of feature selection operations may include at least
one of a correlation operation, a model-based operation, and a feature
reduction operation.
[041] Further, identification at the feature selection stage includes
generating for each of the plurality of feature selection operations, the feature
subset from the associated set of predefined feature selection methods at
step 312. The feature subset may be generated based on the result of
processing through each of the second plurality of recommendation layers.
Additionally, for each of the plurality of feature selection operations, a rank
may be assigned to each predefined feature selection method in the
associated feature subset, at step 314.
[042] In an embodiment, the process 300 may include identifying at
a model training stage, for each of multiple model training operations, a
training subset from an associated set of predefined model training methods
for the predefined objective at step 316. It may be noted that each training
subset may include a list of ranked predefined model training methods. In an
exemplary embodiment, the multiple model training operations include at
least one of an algorithm selection operation, a hyperparameters tuning
operation, and a model optimization operation. Further, at step 318, the
training subset may be generated for each of the multiple model training
operations from the associated set of predefined model training methods. The
training subset may be generated based on the result of processing through
each of the third plurality of recommendation layers. The third plurality of
recommendation layers may also include a pre-defined statistical and
quantitative method layer, a pre-defined rules and practices layer, and a
predefined meta learning-based layer. Additionally, at step 320, a rank may
be assigned to each predefined model training method in the associated
training subset.
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[043] In another exemplary embodiment, the data analytics tuple
generating module 208 may generate a plurality of data analytics tuples at
step 322. It may be noted that each of the plurality of data analytics tuples
may include a predefined pre-processing method selected from the
associated pre-processing subset, a predefined feature selection method
selected from the associated feature subset, and a predefined model training
method selected from the associated training subset. In yet another
exemplary embodiment, the data analytics tuple selecting module 210 may
select a data analytics tuple from the plurality of data analytics tuples at step
324. It may be noted that an output result of the data analytics tuple may
include highest ranked results for the predefined objective. In addition, the
data analytics tuple may correspond to the data analytics model.
[044] .
[045] Referring now to FIG. 4, at 400, an exemplary functioning of the
recommender device 102 of the system 100, at the data pre-processing
stage, is illustrated, in accordance with some embodiments. With reference
to FIG. 4, a data pre-processing recommender 414 may receive as input – an
input data, different choices made by a user from options available for each
set of pre-processing options at block 412, and a problem type (e.g.,
classification, regression, forecasting, etc.)
[046] To optimize the set of choices for a given problem, the data preprocessing recommender 414 may take in all the options and may give out
best set of choices. The data pre-processing options, at block 412, may
include an impute operation at block 402. The impute operation at block 402
may include methods such as mean, median, mode, K-Nearest Neighbors
(KNN), etc. Additionally, the impute operation at block 402 may include
methods such as to discard entries with missing values in their attributes, use
maximum likelihood procedures, where the parameters of a model for
complete data are estimated, and later used for imputation by means of
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sampling, and by imputation of missing values that may be based on a set of
procedures that aims to fill in missing values with estimated ones.
[047] At block 404, an outlier detection operation may include
methods that may be based on statistical analysis techniques such as Interquartile range, z-score etc. and model based techniques such as Isolation
Forest (I.Forest), regression analysis etc. At block 406, an outlier treatment
operation may include methods such as keep, remove, correct, modify, etc.
As an example, choices for outlier detection operation may be based on
statistical analysis techniques such as Inter-quartile range, z-score etc. and
model based techniques such as Isolation Forest, regression analysis etc.
Further, at block 408, rescale operation may include methods such as
normalize, re-scale, rank, min-max, scale (0-1), Zscore etc. Additionally, at
block 410, transform operation may include methods such as bin, one hot
encode, label encode, hashing, etc. As may be appreciated, by using the data
pre-processing options, at block 412, there may be availability of a huge
number of options in data pre-processing stage itself, and if a combination of
the options is taken together, a total number of possibilities may be much
higher, as total number of choices may be a product of the possibilities.
[048] Thereafter, the data pre-processing recommender 414 may
rank the received choices for each of the operation (e.g., the impute operation
at block 402, the outlier detection operation at block 404, an outlier treatment
operation at block 406, the rescale operation at block 408, the transform
operation at block 410) based on a criteria appropriate for each of the
operation. In an embodiment, the data pre-processing recommender 414
may result in generation of a selected imputation method, at block 416, for
the corresponding impute operation, at block 402, using, for example, an
imputation method recommender. Further, a selected outlier detection
method, at block 418, may be generated for the corresponding outlier
detection operation, at block 404, using, for example, an outlier detection
recommender. Additionally, a selected outlier treatment method, at block 420,
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may be generated for the outlier treatment operation, at block 406, using, for
example, an outlier treatment recommender. Further, a selected rescaling
method, at block 422, may be generated for the rescale operation, at block
408, using, for example, a rescaling recommender. Additionally, a selected
transformation method, at block 424, may be generated for the transformation
operation, at block 410, using, for example, a transformation recommender.
[049] In another embodiment, a criteria for imputation method ranking
may be based on a least noise induced and by ones that maintain close
relationship with a target. Further, a criteria for outlier detection method
ranking may be based on how the outliers may be categorized into different
types. Furthermore, a criteria for outlier treatment method ranking may be
based on noise and influence of treatment on the target. Additionally, criteria
for rescaling and transformation method ranking may be based on
importance of the features. It may be noted that top N ranks (e.g., depend on
how many configurable choices are required by the user) may be a plurality
of selected strategies that may be availed by the user. The top N ranks may
be provided as a list of choices for each set of operations that may be ranked
in an order of preference which the user may use to run the pipeline.
[050] In an embodiment, the data pre-processing recommender 414
may include, but is not limited to three main components. These three main
components may correspond to the first plurality of recommendation layers.
The data pre-processing recommender 414 may provide recommendations
to the user on a best set of choices for each of the operation based on the
three components. One of the components may facilitate to use statistical
methods and quantitative methods, that may include methods (but not limited
to) (a) determining influence of the operation on the data with respect to
quantifying the noise induced by the method, (b) determining a degree of
change in relationship of the data with respect to the target due to the
corresponding operation, and (c) performing standard deviation analysis and
/ or percentage analysis due to the operation. Another component may
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facilitate to use well defined rules and best practices, with respect to aspects
such as but not limited to, (a) stabilizing the variances, (b) linearize
relationships, (c) data transformations based on the problem type, (d) types
of outliers, and (e) sampling and discretization. Yet another component may
facilitate to use meta learning-based approaches, that include but are not
limited to (a) estimating degree of change based on scores of predictors and
based on residual scores, (b) estimation using regression and modeling
techniques, and (c) estimating degree of importance based on results of
algorithms.
[051] Referring now to FIG. 5, at 500, an exemplary functioning of the
recommender device 102 of the system 100, at the feature selection stage,
is illustrated, in accordance with some embodiments. With reference to FIG.
5, a feature selection recommender 510 may receive as input – a list of
choices for each set of feature selection options available at block 508, preprocessed data received from the data pre-processing stage, and a type of
problem (e.g., classification, regression, forecasting etc.). To optimize the
set of choices for a given problem, the feature selection recommender 510
may take in all the options and may give out best set of choices.
[052] The feature selection options, at block 508, may categorize
operations in multiple (e.g., three) broad categories. The categories for the
operations may be such as a correlation or statistical based feature selection
operation at block 502, a model based or meta learning based feature
selection operation at block 504, and a feature reduction or dimensionality
reduction operation at block 506. In an embodiment, the correlation
operation, at block 502, may include methods such as Chi Square, mutual
information, Anova (F-Test), etc. At block 504, the model based operation
may include methods such as extra tree classifier, lasso, random forest etc.
Further, at block 506, the feature reduction operation may include methods
such as missing value ratio, low variance, high correlation, Principal
Component Analysis (PCA), Singular Value Decomposition (SVD), etc. As
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may be appreciated, by using the feature selection options, at block 508,
there may be availability of a huge number of options in data feature selection
stage, and if a combination of the options is taken together, a total number of
possibilities may be much higher, as total number of choices may be a
product of the possibilities.
[053] Thereafter, the feature selection recommender 510 may rank
the received choices for each of the operation (e.g., the correlation operation
at block 502, a model based or meta learning based feature selection
operation at block 504, and a feature reduction or dimensionality reduction
operation at block 506) based on a criteria appropriate for each of the
operation. In an embodiment, the feature selection recommender 510 may
result in generation of a selected features correlation method, at block 512,
for the corresponding correlation operation, at block 502, using for example,
a correlation method recommender. Further, a selected features method, at
block 514, may be generated for the corresponding model based operation,
at block 504, using, for example, a model based recommender. Additionally,
a selected features reduced method, at block 516, may be generated for the
corresponding feature reduction operation, at block 506, using, for example,
a feature reduction recommender.
[054] In another embodiment, a criteria for correlation-based features
may be set as per a correlation threshold selected by the user. Further, a
criteria for model-based feature selection may be established as per a degree
of importance threshold selected by the user. Furthermore, a criteria for
feature reduction may be based on a percentage of variance to be captured
as selected by the user. It may be noted that top N ranks (e.g., depend on
how many configurable choices are required by the user) may be multiple
selected strategies that may be availed by the user. The top N ranks may be
provided as a list of choices for each set of operations that may be ranked in
an order of preference which the user may use to run the pipeline.
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[055] In an embodiment, the feature selection recommender 510 may
have but not limited to three main components. These three main
components may correspond to the second plurality of recommendation
layers. The feature selection recommender 510 may provide
recommendations to the user on a best set of choices for each of the
operation based on the three components. One of the component may
facilitate to use statistical methods and quantitative methods, that may
include methods (but not limited to) (a) determining cross correlation between
features, (b) determining correlation with respect to the target, (c) determining
cardinality of the features, and (d) variance of the features. Another
component may facilitate to use well defined rules and best practices, with
respect to aspects such as but not limited to, (a) stabilizing the variances, (b)
linearize relationships, (c) data transformations based on the problem type,
(d) types of outliers, and (e) sampling and discretization. Yet another
component may facilitate to use meta learning-based approaches, that
include but are not limited to (a) estimation features to be selected based on
components of the features, and (b) estimating degree of importance based
on modelling techniques.
[056] Referring now to FIG. 6, at 600, an exemplary functioning of
the recommender device 102 of the system 100, at the model training stage,
is illustrated, in accordance with some embodiments. With reference to FIG.
6, a model training recommender 610 may receive as input – a list of choices
for each set of model training options available at block 608, transformed data
received from the feature selection stage, and a type of problem (e.g.,
classification, regression, forecasting etc.). To optimize the set of choices for
a given problem, the model training recommender 610 may take in all the
options and may give out best set of choices.
[057] The model training options, at block 608, may categorize
operations in multiple (e.g., three) broad categories. The categories for the
operations may be such as an algorithm(s) operation at block 602, a hyper
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parameter(s) operation at block 604, and a model tuning operation at block
606. In an embodiment, the algorithms (t) operation at block 602, may include
methods such logisticregression, naivebayes, randomforest, Convolution
Neural Network (CNN)/ Relevance Vector Machine (RVM), Support Vector
Machine (SVM), etc. At block 604, the hyper parameters (s) operation may
include methods such as a model specific, sampling based, scoring criteria
based, iteration based, etc. Further, at block 606, the model tuning operation
(u) may include methods such as gridsearch, randomsearch,
bayesianoptimization, GA, etc. As may be appreciated, by using model
training options, at block 608, there may be availability of a huge number of
options at the model training stage. Also, additional options may be available
from the earlier discussed data pre-processing stage and the feature
selection stage. At the model training stage, a model training recommender
may take in all the options and may give out a best set of choices.
[058] Thereafter, the model training recommender 610 may rank the
received choices for each of the operation (e.g., the algorithms operation at
block 602, a hyperparameters operation at block 604, and a model tuning
operation at block 506) based on a criteria appropriate for each of the
operation. In an embodiment, the model training recommender 610 may
result in generation of a selected algorithms method, at block 612, for the
corresponding algorithms operation, at block 602, using for example, an
algorithm selection recommender. Further, a selected hyper parameters
method, at block 614, may be generated for the corresponding
hyperparameters operation, at block 604, using, for example, a hyper
parameter recommender. Additionally, a selected tuning method, at block
616, may be generated for the corresponding model tuning operation, at block
606, using, for example, a model tuning recommender.
[059] In another embodiment, the model training recommender 610
may rank the available choices of algorithms and top N ranks (e.g., depend
on how many configurable choices are required by the user) may be a
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plurality of selected strategies that may be availed by the user. For the
selected list of algorithms, the model training recommender 610 may also
rank the hyper-parameters and the tuning methods. The user may then again
select top ‘X’ methods out of the ranked hyper-parameters and the tuning
methods. The ranked methods may be provided as a list of choices for each
set of operations. The methods may be ranked in an order of preference
which the user may use to run the pipeline.
[060] In an embodiment, the model training recommender 610 may
have but not limited to three main components. These three main
components may correspond to the third plurality of recommendation layers.
The model training recommender 610 may provide recommendations to the
user on a best set of choices for each of the operation based on the three
components. One of the component may facilitate to use metrics that may
pertain to an algorithm, and may include (but not limited to): (a) type of scoring
criteria selected, (b) degree to which the results match the scoring criteria, (c)
degree to which other metrics may deviate, and (d) bias-variance threshold
that may be permitted. Another component may facilitate an algorithm
selection, with respect to following pointers such as but not limited to, (a) size
of data, (b) linearity of data, (c) nature of target, (d) computational constraint,
and (e) explain-ability and interpretability of the model. Yet another
component may facilitate tuning method selection, that include but are not
limited to (a) degree to which the results may converge, (b) computational
complexity, and (c) using benchmarks and comparisons with historical data.
[061] Referring now to FIG. 7, an exemplary representation of a
transformation options pipeline is illustrated at 700, in accordance with some
embodiments. With respect to FIG. 7, all possible combinations of options are
passed as input data (at block 706). The input data may be determined at
data pre-processing stage (at block 702) and at feature selection stage (at
block 704) through their corresponding recommenders to generate a set of
transformed data, represented as a transformed data matrix at block 708.
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Further, all possible combination of options may be depicted as a matrix
including multiple possible combinations. Thereafter, the system may list
down and expose all possible data vectors determined in the matrix to
participate in a model training process.
[062] Referring now to FIG. 8, an exemplary representation of a
pipeline tuning matrix is illustrated at 800, in accordance with some
embodiments. In an exemplary embodiment, if at the data pre-processing
stage a total number of options are say, for example, ‘D’ possible options
(represented at block 802), and if at the feature selection stage, the total
number of options are say, for example, ‘F’ possible options (represented at
block 802), and if the model training stage has a total of say, for example, ‘M’
possible options, a pipeline parameter generation matrix may be generated
(at block 802). Further, the set of transformed data (at block 806), generated
from the pipeline parameter generation matrix may be sent through a model
training recommender with determined model training options (at block 804),
to obtain a best model (at block 808).
[063] In an embodiment, the pipeline tuning matrix 800 may be
generated based on a pipeline tuning algorithm. The algorithm may follow the
following series of steps: (a) receiving input data, (b) receiving user input on
all data pre-processing options, feature selection options, and model training
options that needs to be evaluated. The options may be received in form of a
configuration file or in any other format, (c) using the received data preprocessing options to create an entire set of data vectors as outlined in the
data pre-processing stage using a data pre-processing recommender, (d)
using the feature selection options to create an entire set of feature vectors
as an outline in the feature selection stage using the feature selection
recommender system, (e) running the generated transformed data through
the model training stage as outlined using the model training recommender
system for each of the tuples and store the result of each iteration, and (f)
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choosing the data pre-processing, feature selection and model tuning tuple
that generates best results.
[064] As will be also appreciated, the above described techniques
may take the form of computer or controller implemented processes and
apparatuses for practicing those processes. The disclosure can also be
embodied in the form of computer program code containing instructions
embodied in tangible media, such as floppy diskettes, solid state drives, CDROMs, hard drives, or any other computer-readable storage medium,
wherein, when the computer program code is loaded into and executed by a
computer or controller, the computer becomes an apparatus for practicing the
invention. The disclosure may also be embodied in the form of computer
program code or signal, for example, whether stored in a storage medium,
loaded into and/or executed by a computer or controller, or transmitted over
some transmission medium, such as over electrical wiring or cabling, through
fiber optics, or via electromagnetic radiation, wherein, when the computer
program code is loaded into and executed by a computer, the computer
becomes an apparatus for practicing the invention. When implemented on a
general-purpose microprocessor, the computer program code segments
configure the microprocessor to create specific logic circuits.
[065] The disclosed methods and systems may be implemented on a
conventional or a general-purpose computer system, such as a personal
computer (PC) or server computer. Referring now to FIG. 9, an exemplary
computing system 900 that may be employed to implement processing
functionality for various embodiments (e.g., as a SIMD device, client device,
server device, one or more processors, or the like) is illustrated. Those skilled
in the relevant art will also recognize how to implement the invention using
other computer systems or architectures. The computing system 900 may
represent, for example, a user device such as a desktop, a laptop, a mobile
phone, personal entertainment device, DVR, and so on, or any other type of
special or general-purpose computing device as may be desirable or
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appropriate for a given application or environment. The computing system
900 may include one or more processors, such as a processor 902 that may
be implemented using a general or special purpose processing engine such
as, for example, a microprocessor, microcontroller or other control logic. In
this example, the processor 902 is connected to a bus 904 or other
communication medium. In some embodiments, the processor 902 may be
an Artificial Intelligence (AI) processor, which may be implemented as a
Tensor Processing Unit (TPU), or a graphical processor unit, or a custom
programmable solution Field-Programmable Gate Array (FPGA).
[066] The computing system 900 may also include a memory 906
(main memory), for example, Random Access Memory (RAM) or other
dynamic memory, for storing information and instructions to be executed by
the processor 902. The memory 906 also may be used for storing temporary
variables or other intermediate information during execution of instructions to
be executed by the processor 902. The computing system 900 may likewise
include a read only memory (“ROM”) or other static storage device coupled
to bus 904 for storing static information and instructions for the processor
902.
[067] The computing system 900 may also include a storage devices
908, which may include, for example, a media drive 910 and a removable
storage interface. The media drive 910 may include a drive or other
mechanism to support fixed or removable storage media, such as a hard disk
drive, a floppy disk drive, a magnetic tape drive, an SD card port, a USB port,
a micro USB, an optical disk drive, a CD or DVD drive (R or RW), or other
removable or fixed media drive. A storage media 912 may include, for
example, a hard disk, magnetic tape, flash drive, or other fixed or removable
medium that is read by and written to by the media drive 910. As these
examples illustrate, the storage media 912 may include a computer-readable
storage medium having stored therein particular computer software or data.
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[068] In alternative embodiments, the storage devices 908 may
include other similar instrumentalities for allowing computer programs or
other instructions or data to be loaded into the computing system 900. Such
instrumentalities may include, for example, a removable storage unit 914 and
a storage unit interface 916, such as a program cartridge and cartridge
interface, a removable memory (for example, a flash memory or other
removable memory module) and memory slot, and other removable storage
units and interfaces that allow software and data to be transferred from the
removable storage unit 914 to the computing system 900.
[069] The computing system 900 may also include a communications
interface 918. The communications interface 918 may be used to allow
software and data to be transferred between the computing system 900 and
external devices. Examples of the communications interface 918 may include
a network interface (such as an Ethernet or other NIC card), a
communications port (such as for example, a USB port, a micro USB port),
Near field Communication (NFC), etc. Software and data transferred via the
communications interface 918 are in the form of signals which may be
electronic, electromagnetic, optical, or other signals capable of being
received by the communications interface 918. These signals are provided to
the communications interface 918 via a channel 920. The channel 920 may
carry signals and may be implemented using a wireless medium, wire or
cable, fiber optics, or other communications medium. Some examples of the
channel 920 may include a phone line, a cellular phone link, an RF link, a
Bluetooth link, a network interface, a local or wide area network, and other
communications channels.
[070] The computing system 900 may further include Input/Output
(I/O) devices 922. Examples may include, but are not limited to a display,
keypad, microphone, audio speakers, vibrating motor, LED lights, etc. The
I/O devices 922 may receive input from a user and also display an output of
the computation performed by the processor 902. In this document, the terms
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“computer program product” and “computer-readable medium” may be used
generally to refer to media such as, for example, the memory 906, the storage
devices 908, the removable storage unit 914, or signal(s) on the channel 920.
These and other forms of computer-readable media may be involved in
providing one or more sequences of one or more instructions to the processor
902 for execution. Such instructions, generally referred to as “computer
program code” (which may be grouped in the form of computer programs or
other groupings), when executed, enable the computing system 900 to
perform features or functions of embodiments of the present invention.
[071] In an embodiment where the elements are implemented using
software, the software may be stored in a computer-readable medium and
loaded into the computing system 900 using, for example, the removable
storage unit 914, the media drive 910 or the communications interface 918.
The control logic (in this example, software instructions or computer program
code), when executed by the processor 902, causes the processor 902 to
perform the functions of the invention as described herein.
[072] Thus, the disclosed method and system overcomes the
technical problem of selecting pre-processing and feature selection options
by the user. The method and system may facilitate the user to know about
entire pipeline parameters so that the user may know about available best
pre-processing and feature selection options to be selected to generate a
best possible model. Further, by logging all results of all possible
combinations across multiple datasets, a meta learning approach may be
used to select most optimal pipeline parameters. As may be appreciated, the
disclosed system and method provides a very exhaustive approach to
achieve most optimal results.
[073] As will be appreciated by those skilled in the art, the techniques
described in the various embodiments discussed above are not routine, or
conventional, or well understood in the art. The techniques discussed above
provide for recommending tuning of parameters to generate a data analytics
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model. The techniques first identify at a data pre-processing stage for each
of a plurality of pre-processing operations, a pre-processing subset from an
associated set of predefined pre-processing methods for a predefined
objective. Each of pre-processing subset includes. a list of ranked predefined
pre-processing methods. The techniques may then identify at a feature
selection stage for each of a plurality of feature selection operations, a feature
subset from an associated set of predefined feature selection methods for the
predefined objective. Each feature subset includes a list of ranked predefined
feature selection methods. The techniques may then identify at a model
training stage for each of a plurality of model training operations, a training
subset from an associated set of predefined model training methods for the
predefined objective. Each training subset includes a list of ranked predefined
model training methods. The technique may then generate a plurality of data
analytics tuples. Each of the plurality of data analytics tuples includes a
predefined pre-processing method selected from the associated preprocessing subset, a predefined feature selection method selected from the
associated feature subset, and a predefined model training method selected
from the associated training subset. Further, the technique may select a data
analytics tuple from the multiple data analytics tuples. An output result of the
data analytics tuple includes highest ranked results for the predefined
objective, and the data analytics tuple corresponds to the data analytics
model.
[074] In light of the above mentioned advantages and the technical
advancements provided by the disclosed method and system, the claimed
steps as discussed above are not routine, conventional, or well understood
in the art, as the claimed steps enable the following solutions to the existing
problems in conventional technologies. Further, the claimed steps clearly
bring an improvement in the functioning of the device itself as the claimed
steps provide a technical solution to a technical problem.
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[075] The specification has described method and system for
identifying vulnerabilities and security risks in an application. The illustrated
steps are set out to explain the exemplary embodiments shown, and it should
be anticipated that ongoing technological development will change the
manner in which particular functions are performed. These examples are
presented herein for purposes of illustration, and not limitation. Further, the
boundaries of the functional building blocks have been arbitrarily defined
herein for the convenience of the description. Alternative boundaries can be
defined so long as the specified functions and relationships thereof are
appropriately performed. Alternatives (including equivalents, extensions,
variations, deviations, etc., of those described herein) will be apparent to
persons skilled in the relevant art(s) based on the teachings contained herein.
Such alternatives fall within the scope and spirit of the disclosed
embodiments.
[076] Furthermore, one or more computer-readable storage media
may be utilized in implementing embodiments consistent with the present
disclosure. A computer-readable storage medium refers to any type of
physical memory on which information or data readable by a processor may
be stored. Thus, a computer-readable storage medium may store instructions
for execution by one or more processors, including instructions for causing
the processor(s) to perform steps or stages consistent with the embodiments
described herein. The term “computer-readable medium” should be
understood to include tangible items and exclude carrier waves and transient
signals, i.e., be non-transitory. Examples include random access memory
(RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard
drives, CD ROMs, DVDs, flash drives, disks, and any other known physical
storage media.
[077] It is intended that the disclosure and examples be considered
as exemplary only, with a true scope and spirit of disclosed embodiments
being indicated by the following claims.

CLAIMS
What is claimed is:
1. A method (300) for recommending tuning of parameters to generate a
data analytics model, the method comprising:
identifying at a data pre-processing stage (304), by a recommender
device (102), for each of a plurality of pre-processing operations, a preprocessing subset from an associated set of predefined pre-processing
methods for a predefined objective, wherein each pre-processing subset
comprises a list of ranked predefined pre-processing methods;
identifying at a feature selection stage (310), by the recommender
device (102), for each of a plurality of feature selection operations, a feature
subset from an associated set of predefined feature selection methods for
the predefined objective, wherein each feature subset comprises a list of
ranked predefined feature selection methods;
identifying at a model training stage (316), by the recommender
device (102), for each of a plurality of model training operations, a training
subset from an associated set of predefined model training methods for the
predefined objective, wherein each training subset comprises a list of
ranked predefined model training methods;
generating (322), by the recommender device (102), a plurality of
data analytics tuples, wherein each of the plurality of data analytics tuples
comprises a predefined pre-processing method selected from the
associated pre-processing subset, a predefined feature selection method
selected from the associated feature subset, and a predefined model
training method selected from the associated training subset; and
selecting (324), by the recommender device (102), a data analytics
tuple from the plurality of data analytics tuples, wherein an output result of
the data analytics tuple comprises highest ranked results for the predefined
objective, and wherein the data analytics tuple corresponds to the data
analytics model.
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2. The method of claim 1, further comprising transforming (302) an input
data to generate transformed data based on the predefined objective.
3. The method of claim 2, wherein transforming (302) the input data
comprises processing at the data pre-processing stage, through a first
plurality of recommendation layers, the input data, a plurality of sets of
predefined pre-processing methods, and a list of problem types associated
with the predefined objective, to generate a set of pre-processed data,
wherein each set in the plurality of sets of predefined pre-processing
methods corresponds to a pre-processing operation from the plurality of preprocessing operations.
4. The method of claim 3, wherein identifying at the data pre-processing
stage (304) comprises:
generating for each of the plurality of pre-processing operations
(306), the pre-processing subset from the associated set of predefined preprocessing methods, based on the result of processing through each of the
first plurality of recommendation layers, wherein the first plurality of
recommendation layers comprises at least one of a pre-defined statistical
and quantitative method layer, a pre-defined rules and practices layer, and
a predefined meta learning-based layer, and wherein the rank is assigned
to each predefined pre-processing method based on an associated criteria;
and
assigning (308), for each of the plurality of pre-processing
operations, a rank to each predefined pre-processing method in the
associated pre-processing subset.
5. The method of claim 2, wherein transforming (302) the input data to
generate the transformed data further comprises processing at the feature
selection stage, through a second plurality of recommendation layers, the
set of pre-processed data, the plurality of sets of predefined feature
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selection methods, and the list of problem types, to generate a transformed
data, wherein each set in the plurality of sets of predefined feature selection
methods corresponds to a feature selection operation from a plurality of
feature selection operations.
6. The method of claim 5, wherein identifying at the feature selection stage
(310) comprises:
generating for each of the plurality of feature selection operations
(312), the feature subset from the associated set of predefined feature
selection methods, based on the result of processing through each of the
second plurality of recommendation layers, wherein the second plurality of
recommendation layers comprises at least one of a pre-defined statistical
and quantitative method layer, a pre-defined rules and practices layer, and
a predefined meta learning-based layer, and wherein the rank is assigned
to each predefined feature selection method based on an associated
criteria; and
assigning (314), for each of the plurality of feature selection
operations, a rank to each predefined feature selection method in the
associated feature subset.
7. The method of claim 5, further comprises processing at a model training
stage, through a third plurality of recommendation layers, the transformed
data, the plurality of sets of predefined model training methods, and the list
of problem types, wherein each set in the plurality of sets of predefined
model training methods corresponds to a model training operation from a
plurality of model training operations, and wherein identifying at the model
training stage comprises generating (318) for each of the plurality of model
training operations, the training subset from the associated set of predefined
model training methods, based on the result of processing through each of
the third plurality of recommendation layers; and assigning (320), for each
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of the plurality of model training operations, a rank to each predefined model
training method in the associated training subset.
8. The method of claim 7, wherein the third plurality of recommendation
layers comprises at least one of a pre-defined statistical and quantitative
method layer, a pre-defined rules and practices layer, and a predefined
meta learning-based layer, and wherein the rank is assigned to each
predefined model training method based on an associated criteria.
9. The method of claim 1, wherein:
the plurality of pre-processing operations comprises at least one of
an impute operation, an outlier detection operation, an outlier treatment
operation, a rescale operation, and a transform operation;
the plurality of feature selection operations comprises at least one of
a correlation operation, a model-based operation, and a feature reduction
operation; and
the plurality of model training operations comprises at least one of an
algorithm selection operation, a hyperparameters tuning operation, and a
model optimization operation.
10. A system (100) for recommending tuning of parameters to generate a
data analytics model, the system (100) comprising:
a recommender device (102) comprising a processor (104) and a
memory communicatively coupled to the processor (104), wherein the
memory stores processor-executable instructions, which, on execution,
causes the processor (104) to:
identify at a data pre-processing stage (304) for each of a
plurality of pre-processing operations, a pre-processing subset from
an associated set of predefined pre-processing methods for a
predefined objective, wherein each pre-processing subset comprises
a list of ranked predefined pre-processing methods;
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identify at a feature selection stage (310) for each of a plurality
of feature selection operations, a feature subset from an associated
set of predefined feature selection methods for the predefined
objective, wherein each feature subset comprises a list of ranked
predefined feature selection methods;
identify at a model training stage (316) for each of a plurality
of model training operations, a training subset from an associated set
of predefined model training methods for the predefined objective,
wherein each training subset comprises a list of ranked predefined
model training methods;
generate (322) a plurality of data analytics tuples, wherein
each of the plurality of data analytics tuples comprises a predefined
pre-processing method selected from the associated pre-processing
subset, a predefined feature selection method selected from the
associated feature subset, and a predefined model training method
selected from the associated training subset; and
select (324) a data analytics tuple from the plurality of data
analytics tuples, wherein an output result of the data analytics tuple
comprises highest ranked results for the predefined objective, and
wherein the data analytics tuple corresponds to the data analytics
model.