1. A method for combining traditional statistics and machine learning to analyse data, comprising: receiving a dataset comprising a plurality of variables; performing a traditional statistical analysis on the dataset to identify significant variables and their relationships; determining a set of machine learning algorithms suitable for analysing the dataset; generating a set of features based on the significant variables identified in the traditional statistical analysis; applying the set of machine learning algorithms to the generated set of features to obtain a set of model predictions; combining the set of model predictions with the results of the traditional statistical analysis to obtain a combined output; and providing the combined output as an indication of the relationships between the significant variables and their impact on the data.

2. The method of claim 1, wherein the traditional statistical analysis comprises regression analysis, correlation analysis, or analysis of variance.

3. The method of claim 1, wherein the set of machine learning algorithms is determined based on the type and characteristics of the dataset.

4. The method of claim 3, wherein the set of machine learning algorithms includes one or more of decision trees, neural networks, support vector machines, or random forests.

5. The method of claim 1, wherein generating a set of features comprises selecting a subset of the significant variables and engineering new features based on them.

6. The method of claim 1, wherein applying the set of machine learning algorithms comprises training each algorithm on a portion of the dataset and validating the trained models on a separate portion of the dataset.

7. The method of claim 1, wherein combining the set of model predictions with the results of the traditional statistical analysis comprises weighting the results of the traditional statistical analysis based on the accuracy of the machine learning algorithms.

8. A system for combining traditional statistics and machine learning to analyse data, comprising: a data receiver configured to receive a dataset comprising a plurality of variables; a processor configured to execute instructions for: performing a traditional statistical analysis on the dataset to identify significant variables and their relationships; determining a set of machine learning algorithms suitable for analyzing the dataset; generating a set of features based on the significant variables identified in the traditional statistical analysis; applying the set of machine learning algorithms to the generated set of features to obtain a set of model predictions; combining the set of model predictions with the results of the traditional statistical analysis to obtain a combined output; a memory for storing the dataset and the combined output; and a display device configured to display the combined output.

9. The system of claim 8, wherein the processor is further configured to receive user input for adjusting the parameters used in the method. A Hybrid Statistical Learning Approach for Combining Traditional Statistics and Machine Learning Abstract The present invention is a system for combining traditional statistics and machine learning to analyze data. The system includes a data receiver, a processor, a memory, and a display device. The data receiver is configured to receive a dataset comprising a plurality of variables. The processor executes instructions for performing a traditional statistical analysis on the dataset to identify significant variables and their relationships. The processor also determines a set of machine learning algorithms suitable for analyzing the dataset, generates a set of features based on the significant variables identified in the traditional statistical analysis, and applies the set of machine learning algorithms to the generated set of features to obtain a set of model predictions. The set of model predictions is then combined with the results of the traditional statistical analysis to obtain a combined output. The memory stores the dataset and the combined output, while the display device displays the combined output. The system is highly versatile and can be used for analyzing various types of data, such as marketing data or manufacturing data. Additionally, the processor can receive user input for adjusting the parameters used in the method, allowing for customization to suit the specific needs of the user. Overall, the present invention provides a powerful tool for analyzing data and making data-driven decisions. , Claims:Claims :

1. A method for combining traditional statistics and machine learning to analyse data, comprising: receiving a dataset comprising a plurality of variables; performing a traditional statistical analysis on the dataset to identify significant variables and their relationships; determining a set of machine learning algorithms suitable for analysing the dataset; generating a set of features based on the significant variables identified in the traditional statistical analysis; applying the set of machine learning algorithms to the generated set of features to obtain a set of model predictions; combining the set of model predictions with the results of the traditional statistical analysis to obtain a combined output; and providing the combined output as an indication of the relationships between the significant variables and their impact on the data.

2. The method of claim 1, wherein the traditional statistical analysis comprises regression analysis, correlation analysis, or analysis of variance.

3. The method of claim 1, wherein the set of machine learning algorithms is determined based on the type and characteristics of the dataset.

4. The method of claim 3, wherein the set of machine learning algorithms includes one or more of decision trees, neural networks, support vector machines, or random forests.

5. The method of claim 1, wherein generating a set of features comprises selecting a subset of the significant variables and engineering new features based on them.

6. The method of claim 1, wherein applying the set of machine learning algorithms comprises training each algorithm on a portion of the dataset and validating the trained models on a separate portion of the dataset.

7. The method of claim 1, wherein combining the set of model predictions with the results of the traditional statistical analysis comprises weighting the results of the traditional statistical analysis based on the accuracy of the machine learning algorithms.

8. A system for combining traditional statistics and machine learning to analyse data, comprising: a data receiver configured to receive a dataset comprising a plurality of variables; a processor configured to execute instructions for: performing a traditional statistical analysis on the dataset to identify significant variables and their relationships; determining a set of machine learning algorithms suitable for analyzing the dataset; generating a set of features based on the significant variables identified in the traditional statistical analysis; applying the set of machine learning algorithms to the generated set of features to obtain a set of model predictions; combining the set of model predictions with the results of the traditional statistical analysis to obtain a combined output; a memory for storing the dataset and the combined output; and a display device configured to display the combined output.

9. The system of claim 8, wherein the processor is further configured to receive user input for adjusting the parameters used in the method.

Description:A Hybrid Statistical Learning Approach for Combining Traditional Statistics and Machine Learning
Field of the Invention
[0001] The present invention relates to a hybrid statistical learning approach for combining traditional statistics and machine learning techniques to analyze complex data sets. More specifically, the invention provides a novel approach for generating accurate predictions and meaningful insights from data sets that exhibit complex and nonlinear relationships between variables.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Statistical learning is a field that focuses on developing methods for analyzing complex data sets to identify patterns and relationships between variables. Traditional statistical methods have been used for decades in this field to analyze data, but they often have limitations, particularly when it comes to analyzing large, high-dimensional data sets. In recent years, machine learning methods have emerged as a powerful alternative to traditional statistical methods, providing new ways to analyze complex data sets.
[0004] Machine learning algorithms are based on a computational approach that involves identifying patterns in data, learning from those patterns, and then using that learning to make predictions or decisions. Unlike traditional statistical methods, which rely on a pre-specified model, machine learning algorithms can adapt to the data and uncover hidden patterns and relationships between variables.
[0005] Despite the advantages of machine learning, traditional statistical methods remain valuable in many applications. Traditional statistical methods provide a framework for hypothesis testing, model selection, and inference, which are critical for developing a deep understanding of the data and its underlying processes. In addition, traditional statistical methods often have better interpretability than machine learning models, which is important for many applications.
[0006] To combine the strengths of traditional statistical methods and machine learning, a hybrid statistical learning approach has been developed. This approach involves using traditional statistical methods for model building and hypothesis testing, and then incorporating machine learning algorithms for prediction and classification.
[0007] The hybrid statistical learning approach begins with exploratory data analysis using traditional statistical methods, such as regression analysis or analysis of variance. This process involves identifying relationships between variables, assessing the strength of those relationships, and identifying potential outliers or influential observations.
[0008] Next, machine learning algorithms are used to develop predictive models based on the relationships identified in the exploratory data analysis. Machine learning algorithms, such as decision trees, random forests, or neural networks, are particularly useful for developing predictive models when the relationships between variables are complex or nonlinear.
[0009] Finally, the hybrid approach uses traditional statistical methods to evaluate the performance of the machine learning models. This process involves assessing the accuracy of the predictions, evaluating the robustness of the models, and identifying any potential limitations or biases in the models.
[00010] The hybrid statistical learning approach has a wide range of applications in fields such as finance, marketing, healthcare, and many others. In finance, the hybrid approach can be used to develop models for predicting stock prices or market trends. In healthcare, the approach can be used to develop models for predicting disease outcomes or patient responses to treatments.
[00011] Overall, the hybrid statistical learning approach provides a powerful and flexible framework for analyzing complex data sets by combining the strengths of traditional statistical methods and machine learning algorithms. The approach enables researchers and analysts to develop models that are both interpretable and accurate, providing a deep understanding of the data and its underlying processes.
[00012] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.
[00013] It also shall be noted that as used herein and in the appended claims, the singular forms “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise. This invention can be achieved by means of hardware including several different elements or by means of a suitably programmed computer. In the unit claims that list several means, several ones among these means can be specifically embodied in the same hardware item. The use of such words as first, second, third does not represent any order, which can be simply explained as names.
Summary
[00014] Various objects, features, and advantages of the disclosed subject matter can be more fully appreciated with reference to the following detailed description of the disclosed subject matter when considered in connection with the following drawings, in which like reference numerals identify like elements.
[00015] The present invention relates to a hybrid statistical learning approach for combining traditional statistics and machine learning techniques to analyze complex data sets. More specifically, the invention provides a novel approach for generating accurate predictions and meaningful insights from data sets that exhibit complex and nonlinear relationships between variables.
[00016] The present invention relates to a method for combining traditional statistics and machine learning to analyze data. The method involves receiving a dataset comprising a plurality of variables and performing a traditional statistical analysis on the dataset to identify significant variables and their relationships. Next, a set of machine learning algorithms suitable for analyzing the dataset is determined based on the type and characteristics of the dataset.
[00017] A set of features is generated based on the significant variables identified in the traditional statistical analysis. This can involve selecting a subset of the significant variables and engineering new features based on them. The set of machine learning algorithms is then applied to the generated set of features to obtain a set of model predictions. Each algorithm is trained on a portion of the dataset and validated on a separate portion of the dataset.
[00018] The set of model predictions is then combined with the results of the traditional statistical analysis to obtain a combined output. The combined output provides an indication of the relationships between the significant variables and their impact on the data. The results of the traditional statistical analysis are weighted based on the accuracy of the machine learning algorithms to ensure the most accurate and reliable results.
[00019] The traditional statistical analysis can include regression analysis, correlation analysis, or analysis of variance. The set of machine learning algorithms can include decision trees, neural networks, support vector machines, or random forests.
[00020] The method is highly versatile and can be applied to any type of dataset, allowing for a deep and thorough analysis of complex data. By combining traditional statistical analysis with machine learning, the method provides a more accurate and reliable analysis, allowing for better decision making and improved outcomes in various fields, such as healthcare, finance, and marketing.
[00021] In summary, the method for combining traditional statistics and machine learning to analyze data provides a powerful tool for data analysis. The method involves performing a traditional statistical analysis, generating a set of features, applying a set of machine learning algorithms, combining the set of model predictions with the results of the traditional statistical analysis, and providing a combined output. The method is highly versatile and can be applied to any type of dataset, providing valuable insights and strategies for various applications.
[00022] The system for combining traditional statistics and machine learning to analyze data is a powerful tool for gaining insights and strategies from complex datasets. The system includes a data receiver, a processor, a memory, and a display device.
[00023] The processor is configured to execute instructions for performing a traditional statistical analysis on the dataset to identify significant variables and their relationships, determining a set of machine learning algorithms suitable for analyzing the dataset, generating a set of features based on the significant variables identified in the traditional statistical analysis, applying the set of machine learning algorithms to the generated set of features to obtain a set of model predictions, and combining the set of model predictions with the results of the traditional statistical analysis to obtain a combined output.
[00024] The memory is used to store the dataset and the combined output. The display device is used to display the combined output, which provides an indication of the relationships between the significant variables and their impact on the data.
[00025] The system is highly versatile and can be customized to suit the specific needs of the user by adjusting the parameters used in the analysis. The processor can receive user input for adjusting these parameters, allowing the user to fine-tune the analysis to their specific needs.
[00026] The system allows for the analysis of any type of dataset, including complex and large datasets. By combining traditional statistical analysis with machine learning, the system provides a more accurate and reliable analysis, allowing for better decision making and improved outcomes in various fields, such as healthcare, finance, and marketing.
[00027] In summary, the system for combining traditional statistics and machine learning to analyze data provides a powerful tool for gaining insights and strategies from complex datasets. The system includes a data receiver, a processor, a memory, and a display device. The processor is configured to perform several statistical analyses, including a traditional statistical analysis, machine learning algorithms, and combining the results of these analyses to obtain a combined output. The system is highly versatile and can be customized to suit the specific needs of the user, allowing for a deep and thorough analysis of complex data.
Brief Description of the Drawings
[00028] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00029] FIG. 1 is a flowchart illustrating a method for combining traditional statistics and machine learning to analyse data, according to some embodiments of the present disclosure.
[00030] FIG. 2 represents an exemplary architecture of system for combining traditional statistics and machine learning to analyse data, according to some embodiments of the present disclosure.
Detailed Description
[00031] The following is a detailed description of exemplary embodiments to illustrate the principles of the invention. The embodiments are provided to illustrate aspects of the invention, but the invention is not limited to any embodiment. The scope of the invention encompasses numerous alternatives, modifications and equivalent; it is limited only by the claims.
[00032] In view of the many possible embodiments to which the principles of the present discussion may be applied, it should be recognized that the embodiments described herein with respect to the drawing figures are meant to be illustrative only and should not be taken as limiting the scope of the claims. Therefore, the techniques as described herein contemplate all such embodiments as may come within the scope of the following claims and equivalents thereof.
[00033] Throughout the present disclosure, the term “network” relates to an arrangement of interconnected programmable and/or non-programmable components that are configured to facilitate data communication between one or more electronic devices and/or databases, whether available or known at the time of filing or as later developed. Furthermore, the network may include, but is not limited to, one or more peer-to-peer network, a hybrid peer-to-peer network, local area networks (LANs), radio access networks (RANs), metropolitan area networks (MANS), wide area networks (WANs), all or a portion of a public network such as the global computer network known as the Internet, a private network, a cellular network and any other communication system or systems at one or more locations.
[00034] Throughout the present disclosure, the term “process”* relates to any collection or set of instructions executable by a computer or other digital system so as to configure the computer or the digital system to perform a task that is the intent of the process.
[00035] Throughout the present disclosure, the term ‘Artificial intelligence (AI)’ as used herein relates to any mechanism or computationally intelligent system that combines knowledge, techniques, and methodologies for controlling a bot or other element within a computing environment. Furthermore, the artificial intelligence (AI) is configured to apply knowledge and that can adapt it-self and learn to do better in changing environments. Additionally, employing any computationally intelligent technique, the artificial intelligence (AI) is operable to adapt to unknown or changing environment for better performance. The artificial intelligence (AI) includes fuzzy logic engines, decision-making engines, preset targeting accuracy levels, and/or programmatically intelligent software.
[00036] The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different instances in the description and the figures may indicate similar or identical items.
[00037] The present invention relates to a hybrid statistical learning approach for combining traditional statistics and machine learning techniques to analyze complex data sets. More specifically, the invention provides a novel approach for generating accurate predictions and meaningful insights from data sets that exhibit complex and nonlinear relationships between variables.
[00038] FIG. 1. represents a method 100 for combining traditional statistics and machine learning to analyse data involves integrating the strengths of both approaches to gain a comprehensive understanding of the dataset. The first step (at step 102) involves obtaining the dataset that needs to be analysed. The dataset can be in the form of a structured database or an unstructured dataset such as text or images. For example, the dataset could include patient records, with variables such as age, sex, medical history, and treatment outcomes.
[00039] In an embodiment, (at step 104) performing a traditional statistical analysis on the dataset to identify significant variables and their relationships. In this step, traditional statistical analysis techniques such as linear regression or ANOVA are used to identify the significant variables and their relationships. The aim is to understand the underlying patterns and relationships in the data. For example, in the patient records dataset, traditional statistical analysis could be used to determine whether age, sex, or medical history are significant predictors of treatment outcomes.
[00040] In an embodiment, determining a set of machine learning algorithms suitable for analysing the dataset. The next step (at step 106) involves identifying the appropriate machine learning algorithms to analyse the dataset. This could include algorithms such as decision trees, random forests, or neural networks. For example, in the patient records dataset, a decision tree algorithm could be used to identify subgroups of patients with similar characteristics and treatment outcomes.
[00041] In an embodiment, (at step 108) generating a set of features based on the significant variables identified in the traditional statistical analysis. In this step, a set of features is generated based on the significant variables identified in the traditional statistical analysis. These features can be used as inputs to the machine learning algorithms. For example, in the patient records dataset, the features could include age, sex, medical history, and treatment outcomes.
[00042] In an embodiment, (at step 110) applying the set of machine learning algorithms to the generated set of features to obtain a set of model predictions. The set of machine learning algorithms is applied to the generated set of features to obtain a set of model predictions. These predictions can be used to further understand the relationships between the variables and their impact on the data. For example, the decision tree algorithm could be used to predict treatment outcomes for new patients based on their characteristics and medical history.
[00043] In an embodiment, (at step 112) combining the set of model predictions with the results of the traditional statistical analysis to obtain a combined output. In this step, the results of the machine learning algorithms are combined with the results of the traditional statistical analysis to obtain a combined output. The aim is to obtain a comprehensive understanding of the dataset and its underlying patterns and relationships. For example, the results of the decision tree algorithm could be combined with the results of the traditional statistical analysis to identify which patient characteristics have the greatest impact on treatment outcomes.
[00044] Referring to the preceding embodiment, providing the combined output as an indication of the relationships between the significant variables and their impact on the data. The final step (at step 114) involves providing the combined output as an indication of the relationships between the significant variables and their impact on the data. This output can be used to make informed decisions and gain insights into the dataset. For example, the combined output could be used to identify patient subgroups with similar characteristics and treatment outcomes, which could inform personalized treatment plans.
[00045] In an embodiment, the traditional statistical analysis in this method involves performing various statistical techniques to identify significant variables and their relationships within the dataset. For example, regression analysis involves building a model that shows the relationship between one or more independent variables and a dependent variable. If a dataset of housing prices is provided, could perform a regression analysis to identify the significant variables that impact the prices, such as square footage, number of bedrooms, and location.
[00046] Referring to the preceding embodiment, correlation analysis involves examining the relationship between two or more variables to determine if they are related and, if so, to what degree. For example, if we have a dataset of customer purchases, we could perform a correlation analysis to determine if there is a relationship between the amount spent and the customer's age, gender, or location.
[00047] Referring to the preceding embodiment, analysis of Variance (ANOVA) involves comparing the means of two or more groups to determine if there is a significant difference between them. For example, if we have a dataset of student test scores, we could perform an ANOVA to determine if there is a significant difference in scores between students who studied with a tutor and those who did not. By performing these traditional statistical analyses, we can identify the significant variables and their relationships within the dataset. This information can then be used to generate a set of features that are relevant for the machine learning algorithms.
[00048] FIG. 2 illustrates about a system 200 for combining traditional statistics and machine learning to analyse data comprises a data receiver 202, a processor 204, a memory 206, and a display device 208. The data receiver is configured to receive a dataset comprising a plurality of variables. The processor is configured to execute instructions for performing a traditional statistical analysis on the dataset to identify significant variables and their relationships, determining a set of machine learning algorithms suitable for analysing the dataset, generating a set of features based on the significant variables identified in the traditional statistical analysis, applying the set of machine learning algorithms to the generated set of features to obtain a set of model predictions, and combining the set of model predictions with the results of the traditional statistical analysis to obtain a combined output. The memory is used to store the dataset and the combined output. The display device is used to display the combined output.
[00049] In this embodiment, the system is designed for analysing financial data to identify trends and patterns. The data receiver is configured to receive financial data from various sources, including stocks, bonds, and commodities. The processor performs a traditional statistical analysis, such as regression analysis and correlation analysis, to identify significant variables and their relationships. The processor also determines a set of machine learning algorithms suitable for analysing the financial data, including decision trees and random forests. The processor generates a set of features based on the significant variables identified in the traditional statistical analysis, such as market indices and price changes. The processor applies the set of machine learning algorithms to the generated set of features to obtain a set of model predictions, including predicted stock prices and future market trends. The set of model predictions is then combined with the results of the traditional statistical analysis to obtain a combined output, which provides valuable insights into the financial market trends and patterns. The system is highly versatile and can be customized to suit the specific needs of the user, allowing for a deep and thorough analysis of financial data.
[00050] The system for combining traditional statistics and machine learning to analyse data comprises a data receiver, a processor, a memory, and a display device. The data receiver is configured to receive a dataset comprising a plurality of variables. The processor is configured to execute instructions for performing a traditional statistical analysis on the dataset to identify significant variables and their relationships, determining a set of machine learning algorithms suitable for analysing the dataset, generating a set of features based on the significant variables identified in the traditional statistical analysis, applying the set of machine learning algorithms to the generated set of features to obtain a set of model predictions, and combining the set of model predictions with the results of the traditional statistical analysis to obtain a combined output. The memory is used to store the dataset and the combined output. The display device is used to display the combined output.
[00051] In this embodiment, the system is designed for analysing healthcare data to identify patterns and predict outcomes. The data receiver is configured to receive patient data, including medical history, vital signs, and test results. The processor performs a traditional statistical analysis, such as analysis of variance and t-tests, to identify significant variables and their relationships. The processor also determines a set of machine learning algorithms suitable for analysing the healthcare data, including neural networks and support vector machines. The processor generates a set of features based on the significant variables identified in the traditional statistical analysis, such as patient demographics and medical conditions. The processor applies the set of machine learning algorithms to the generated set of features to obtain a set of model predictions, including predicted diagnoses and disease progression. The set of model predictions is then combined with the results of the traditional statistical analysis to obtain a combined output, which provides valuable insights into the healthcare data and helps medical professionals to make better-informed decisions. The system is highly versatile and can be customized to suit the specific needs of the user, allowing for a deep and thorough analysis of healthcare data.
[00052] The system for combining traditional statistics and machine learning to analyse data comprises a data receiver, a processor, a memory, and a display device. The data receiver is configured to receive a dataset comprising a plurality of variables. The processor is configured to execute instructions for performing a traditional statistical analysis on the dataset to identify significant variables and their relationships, determining a set of machine learning algorithms suitable for analysing the dataset, generating a set of features based on the significant variables identified in the traditional statistical analysis, applying the set of machine learning algorithms to the generated set of features to obtain a set of model predictions, and combining the set of model predictions with the results of the traditional statistical analysis to obtain a combined output. The memory is used to store the dataset and the combined output. The display device is used to display the combined output.
[00053] In this embodiment, the system is designed for analysing marketing data to identify consumer behaviour and preferences. The data receiver is configured to receive marketing data, including customer demographics, purchase history, and social media activity. The processor performs a traditional statistical analysis, such as cluster analysis and factor analysis, to identify significant variables and their relationships. The processor also determines a set of machine learning algorithms suitable for analysing the marketing data, including association rule mining and recommendation engines. The processor generates a set of features based on the significant variables identified in the traditional statistical analysis, such as customer preferences and buying patterns. The processor applies the set of machine learning algorithms to the generated set of features to obtain a set of model predictions, including recommended products and personalized promotions. The set of model predictions is then combined with the results of the traditional statistical analysis to obtain a combined output, which provides valuable insights into consumer behaviour and helps businesses to make data-driven decisions. The system is highly versatile and can be customized to suit the specific needs of the user, allowing for a deep and thorough analysis of marketing data.
[00054] The system for combining traditional statistics and machine learning to analyse data comprises a data receiver, a processor, a memory, and a display device. The data receiver is configured to receive a dataset comprising a plurality of variables. The processor is configured to execute instructions for performing a traditional statistical analysis on the dataset to identify significant variables and their relationships, determining a set of machine learning algorithms suitable for analysing the dataset, generating a set of features based on the significant variables identified in the traditional statistical analysis, applying the set of machine learning algorithms to the generated set of features to obtain a set of model predictions, and combining the set of model predictions with the results of the traditional statistical analysis to obtain a combined output. The memory is used to store the dataset and the combined output. The display device is used to display the combined output.
[00055] In this embodiment, the system is designed for analysing manufacturing data to improve production processes and product quality. The data receiver is configured to receive manufacturing data, including machine performance, production rates, and defect rates. The processor performs a traditional statistical analysis, such as control charts and hypothesis testing, to identify significant variables and their relationships. The processor also determines a set of machine learning algorithms suitable for analysing the manufacturing data, including anomaly detection and predictive maintenance. The processor generates a set of features based on the significant variables identified in the traditional statistical analysis, such as machine performance metrics and defect types. The processor applies the set of machine learning algorithms to the generated set of features to obtain a set of model predictions, including predicted machine failures and recommended maintenance schedules. The set of model predictions is then combined with the results of the traditional statistical analysis to obtain a combined output, which provides valuable insights into manufacturing processes and helps to improve product quality and reduce costs. The system is highly versatile and can be customized to suit the specific needs of the user, allowing for a deep and thorough analysis of manufacturing data.
[00056] The above description is intended to be illustrative, and not restrictive. Although the present disclosure has been described with references to specific illustrative examples and implementations, it will be recognized that the present disclosure is not limited to the examples and implementations described. The scope of the disclosure should be determined with reference to the following claims, along with the full scope of equivalents to which the claims are entitled.
[00057] Modifications, additions, or omissions may be made to the systems and apparatuses described herein without departing from the scope of the disclosure. The components of the systems and apparatuses may be integrated or separated. Moreover, the operations of the systems and apparatuses may be performed by more, fewer, or other components. Additionally, operations of the systems and apparatuses may be performed using any suitable logic comprising software, hardware, and/or other logic. As used in this document, “each” refers to each member of a set or each member of a subset of a set.
[00058] The term “memory,” as used herein relates to a volatile or persistent medium, such as a magnetic disk, or optical disk, in which a computer can store data or software for any duration. Optionally, the memory is non-volatile mass storage such as physical storage media. Furthermore, a single memory may encompass and in a scenario wherein computing system is distributed, the processing, memory and/or storage capability may be distributed as well.
[00059] Throughout the present disclosure, the term ‘server’ relates to a structure and/or module that include programmable and/or non-programmable components configured to store, process and/or share information. Optionally, the server includes any arrangement of physical or virtual computational entities capable of enhancing information to perform various computational tasks.

Claims
I/We Claim:
1. A method for combining traditional statistics and machine learning to analyse data, comprising:
receiving a dataset comprising a plurality of variables;
performing a traditional statistical analysis on the dataset to identify significant variables and their relationships;
determining a set of machine learning algorithms suitable for analysing the dataset;
generating a set of features based on the significant variables identified in the traditional statistical analysis;
applying the set of machine learning algorithms to the generated set of features to obtain a set of model predictions;
combining the set of model predictions with the results of the traditional statistical analysis to obtain a combined output; and
providing the combined output as an indication of the relationships between the significant variables and their impact on the data.
2. The method of claim 1, wherein the traditional statistical analysis comprises regression analysis, correlation analysis, or analysis of variance.
3. The method of claim 1, wherein the set of machine learning algorithms is determined based on the type and characteristics of the dataset.
4. The method of claim 3, wherein the set of machine learning algorithms includes one or more of decision trees, neural networks, support vector machines, or random forests.
5. The method of claim 1, wherein generating a set of features comprises selecting a subset of the significant variables and engineering new features based on them.
6. The method of claim 1, wherein applying the set of machine learning algorithms comprises training each algorithm on a portion of the dataset and validating the trained models on a separate portion of the dataset.
7. The method of claim 1, wherein combining the set of model predictions with the results of the traditional statistical analysis comprises weighting the results of the traditional statistical analysis based on the accuracy of the machine learning algorithms.
8. A system for combining traditional statistics and machine learning to analyse data, comprising:
a data receiver configured to receive a dataset comprising a plurality of variables;
a processor configured to execute instructions for:
performing a traditional statistical analysis on the dataset to identify significant variables and their relationships;
determining a set of machine learning algorithms suitable for analyzing the dataset;
generating a set of features based on the significant variables identified in the traditional statistical analysis;
applying the set of machine learning algorithms to the generated set of features to obtain a set of model predictions;
combining the set of model predictions with the results of the traditional statistical analysis to obtain a combined output;
a memory for storing the dataset and the combined output; and
a display device configured to display the combined output.
9. The system of claim 8, wherein the processor is further configured to receive user input for adjusting the parameters used in the method.

A Hybrid Statistical Learning Approach for Combining Traditional Statistics and Machine Learning
Abstract
The present invention is a system for combining traditional statistics and machine learning to analyze data. The system includes a data receiver, a processor, a memory, and a display device. The data receiver is configured to receive a dataset comprising a plurality of variables. The processor executes instructions for performing a traditional statistical analysis on the dataset to identify significant variables and their relationships. The processor also determines a set of machine learning algorithms suitable for analyzing the dataset, generates a set of features based on the significant variables identified in the traditional statistical analysis, and applies the set of machine learning algorithms to the generated set of features to obtain a set of model predictions. The set of model predictions is then combined with the results of the traditional statistical analysis to obtain a combined output. The memory stores the dataset and the combined output, while the display device displays the combined output. The system is highly versatile and can be used for analyzing various types of data, such as marketing data or manufacturing data. Additionally, the processor can receive user input for adjusting the parameters used in the method, allowing for customization to suit the specific needs of the user. Overall, the present invention provides a powerful tool for analyzing data and making data-driven decisions. , Claims:Claims
I/We Claim:
1. A method for combining traditional statistics and machine learning to analyse data, comprising:
receiving a dataset comprising a plurality of variables;
performing a traditional statistical analysis on the dataset to identify significant variables and their relationships;
determining a set of machine learning algorithms suitable for analysing the dataset;
generating a set of features based on the significant variables identified in the traditional statistical analysis;
applying the set of machine learning algorithms to the generated set of features to obtain a set of model predictions;
combining the set of model predictions with the results of the traditional statistical analysis to obtain a combined output; and
providing the combined output as an indication of the relationships between the significant variables and their impact on the data.
2. The method of claim 1, wherein the traditional statistical analysis comprises regression analysis, correlation analysis, or analysis of variance.
3. The method of claim 1, wherein the set of machine learning algorithms is determined based on the type and characteristics of the dataset.
4. The method of claim 3, wherein the set of machine learning algorithms includes one or more of decision trees, neural networks, support vector machines, or random forests.
5. The method of claim 1, wherein generating a set of features comprises selecting a subset of the significant variables and engineering new features based on them.
6. The method of claim 1, wherein applying the set of machine learning algorithms comprises training each algorithm on a portion of the dataset and validating the trained models on a separate portion of the dataset.
7. The method of claim 1, wherein combining the set of model predictions with the results of the traditional statistical analysis comprises weighting the results of the traditional statistical analysis based on the accuracy of the machine learning algorithms.
8. A system for combining traditional statistics and machine learning to analyse data, comprising:
a data receiver configured to receive a dataset comprising a plurality of variables;
a processor configured to execute instructions for:
performing a traditional statistical analysis on the dataset to identify significant variables and their relationships;
determining a set of machine learning algorithms suitable for analyzing the dataset;
generating a set of features based on the significant variables identified in the traditional statistical analysis;
applying the set of machine learning algorithms to the generated set of features to obtain a set of model predictions;
combining the set of model predictions with the results of the traditional statistical analysis to obtain a combined output;
a memory for storing the dataset and the combined output; and
a display device configured to display the combined output.
9. The system of claim 8, wherein the processor is further configured to receive user input for adjusting the parameters used in the method.