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  3. Introduction to Predictive Analytics in Python
  • 1

    Building Logistic Regression Models

    Free

    In this Chapter, you'll learn the basics of logistic regression: how can you predict a binary target with continuous variables and, how should you interpret this model and use it to make predictions for new examples?

  • 2

    Forward stepwise variable selection for logistic regression

    In this chapter you'll learn why variable selection is crucial for building a useful model. You'll also learn how to implement forward stepwise variable selection for logistic regression and how to decide on the number of variables to include in your final model.

  • 3

    Explaining model performance to business

    Now that you know how to build a good model, you should convince stakeholders to use it by creating appropriate graphs. You will learn how to construct and interpret the cumulative gains curve and lift graph.

  • 4

    Interpreting and explaining models

    In a business context, it is often important to explain the intuition behind the model you built. Indeed, if the model and its variables do not make sense, the model might not be used. In this chapter you'll learn how to explain the relationship between the variables in the model and the target by means of predictor insight graphs.

Initializing

Exercise

Finding the order of variables

The forward stepwise variable selection procedure starts with an empty set of variables, and adds predictors one by one. In each step, the predictor that has the highest AUC in combination with the current variables is selected.

In this exercise you will learn to implement the forward stepwise variable selection procedure. To this end, you can use the next_best function that has been implemented for you. It can be used as follows: 

next_best(current_variables,candidate_variables,target,basetable)

where current_variables is the list of variables that is already in the model and candidate_variables the list of variables that can be added next.

Instructions

100 XP
  • Use the function next_best to calculate the next best variable and assign it to next_variable.
  • Update the current_variables list.
  • Update the candidate_variables list.