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  3. Introduction to Regression with statsmodels in Python
  • 1

    Simple Linear Regression Modeling

    Free

    You’ll learn the basics of this popular statistical model, what regression is, and how linear and logistic regressions differ. You’ll then learn how to fit simple linear regression models with numeric and categorical explanatory variables, and how to describe the relationship between the response and explanatory variables using model coefficients.

  • 2

    Predictions and model objects

    In this chapter, you’ll discover how to use linear regression models to make predictions on Taiwanese house prices and Facebook advert clicks. You’ll also grow your regression skills as you get hands-on with model objects, understand the concept of "regression to the mean", and learn how to transform variables in a dataset.

  • 3

    Assessing model fit

    In this chapter, you’ll learn how to ask questions of your model to assess fit. You’ll learn how to quantify how well a linear regression model fits, diagnose model problems using visualizations, and understand each observation's leverage and influence to create the model.

  • 4

    Simple Logistic Regression Modeling

    Learn to fit logistic regression models. Using real-world data, you’ll predict the likelihood of a customer closing their bank account as probabilities of success and odds ratios, and quantify model performance using confusion matrices.

Initializing

Exercise

Calculating the confusion matrix

A confusion matrix (occasionally called a confusion table) is the basis of all performance metrics for models with a categorical response (such as a logistic regression). It contains the counts of each actual response-predicted response pair. In this case, where there are two possible responses (churn or not churn), there are four overall outcomes.

  1. True positive: The customer churned and the model predicted they would.
  2. False positive: The customer didn't churn, but the model predicted they would.
  3. True negative: The customer didn't churn and the model predicted they wouldn't.
  4. False negative: The customer churned, but the model predicted they wouldn't.

churn and mdl_churn_vs_relationship are available.

Instructions

100 XP
  • Get the actual responses by subsetting the has_churned column of the dataset. Assign to actual_response.
  • Get the "most likely" predicted responses from the model. Assign to predicted_response.
  • Create a DataFrame from actual_response and predicted_response. Assign to outcomes.
  • Print outcomes as a table of counts, representing the confusion matrix. This has been done for you.