Regression Analysis: The good, the bad and untold

Presented at Pydata Delhi 2017

Transcript

1. Regression Analysis: The good,
   the bad and untold
   

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2. INTRODUCTION
   I am Jalem Raj Rohit.
   Works on Devops and Machine Learning full-time.
   Contributes to Julia, Python and Go’s libraries as
   volunteer work, along with moderating the Devops
   and DataScience sites of StackOverflow
   

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3. REGRESSION ANALYSIS
   ● The Good
   ● The Bad
   ● The Untold
   

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4. MOTIVATION
   ● Understanding and appreciating the beauty behind the simplicity of
   Regression Analysis
   ● How can one leverage the age-old practice of regression analysis in the
   Deep Learning world
   ● Doing data-centric data science, and not model-centric.
   

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5. WHY NOT MODEL-CENTRIC?
   ● People change, and so do their data. Models become obsolete everyday
   ● Advanced models require expensive resources
   ● Model might become obsolete, but the data-driven domain knowledge
   doesn’t
   

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6. ● Regression Analysis is a statistical process for estimating the relationships among
   variables.
   ● It helps one understand how the typical value of the dependant variable changes
   when any of the independent variables is varied, while the other independent
   variables are fixed
   What exactly is Regression
   Analysis?
   

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7. IT IS A CONCEPT
   IT IS NOT AN ALGORITHM
   

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8. ● Regression Analysis is the first step for almost every data science problem
   ● Understand the relationship between variables or a set of variables
   ● Thus data-centric
   BACK IN THE DAYS….
   

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9. THESE DAYS...
   

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10. But but why?
    

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11. But but the data is already linearly seper….
    

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12. ● GROUND-BREAKING ACCURACIES
    ● CAN MODEL ALMOST ANY COMPLEX DATASET
    DEEP LEARNING
    

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13. ● BLACK BOXES. (Cannot be used in high-risk domains)
    ● COMPUTATIONALLY EXPENSIVE
    SHORTCOMINGS OF DEEP
    LEARNING
    

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14. ● NO ONE KNOWS WHAT THE DEEP LEARNING NETWORK DOES UNDER
    THE HOOD
    ● NOT FEASIBLE FOR HIGH RISK DOMAINS LIKE FINANCE AND
    HEALTHCARE
    ● NOT EASY TO TWEAK THE MODELS
    BLACK BOXES
    

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15. ● EXPLAINABILITY
    ● HIGHER CONTROL ON THE VARIABLES
    ● HIGH FLEXIBILITY AND VARIETY
    REGRESSION ANALYSIS: THE
    GOOD
    

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16. ● Summary of a regression analysis is very straightforward and easily interpretable
    ● It broadly contains 5 statistics
    EXPLAINABILITY
    

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17. REGRESSION ANALYSIS
    SUMMARY
    

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18. ● It all starts with a null hypothesis
    ● P-value: Lesser the value, greater the statistical significance of the variable.
    Probability that the variable is not relevant in explaining the independent variable
    ● T-value: The higher the t-value, the higher the confidence in rejecting the null
    hypothesis. It is the difference between population’s mean and the null mean
    ● F-value: The higher the test value. The farthest the distribution is from the null
    hypothesis distribution. It is more like a scaled out version of a T-value because it
    can take in more than 1 variable at a time for testing
    EXPLAINABILITY (GOOD AND
    UNTOLD)
    

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19. ● R-squared: Goodness of fit. Depends on the data variance between the fitted line and
    the data
    ● Significance Codes
    EXPLAINABILITY (contd..)
    

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20. ● Each variable can be tweaked and tested while keeping the others constant
    ● Helps understand variable importance and relationship between them
    HIGHER CONTROL
    

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21. ● Lot of flavours available in regression analysis which include Polynomial Regression,
    Spline-based regression analysis
    ● One can also model nonlinear relationships with these advanced algorithms
    HIGH FLEXIBILITY
    AND VARIETY
    

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22. ● Very sensitive to outliers
    ● Cannot model extremely complex relationships
    ● Data need to be studied properly, before choosing a regression method. An art which
    needs to be practised for getting good at
    THE BAD
    

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23. ● BETTER UNDERSTANDING OF THE DATA
    ● ENABLES DATA-CENTRIC DATA SCIENCE
    ● HELPS IN BETTER ARCHITECTURE CREATION AND SELECTION OF
    ACTIVATION FUNCTIONS
    DEEP LEARNING +
    REGRESSION ANALYSIS
    

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24. ● REGRESSION ANALYSIS IS NOT A REPLACEMENT FOR PREDICTION
    ALGORITHMS
    ● IT IS A CRITICAL MISSING STEP IN MODERN DAY DATA SCIENCE
    PIPELINE
    ● MAKING DATA-CENTRIC DATA SCIENCE GREAT AGAIN
    ● BECOMING BETTER DATA SCIENTISTS/ML ENGINEERS
    TAKEAWAYS
    

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25. THANK YOU
    ● Github: Dawny33
    ● Home: jrajrohit.me
    

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