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  3. Intermediate Regression with statsmodels in Python
  • 1

    Parallel Slopes

    Free

    Extend your linear regression skills to parallel slopes regression, with one numeric and one categorical explanatory variable. This is the first step towards conquering multiple linear regression.

  • 2

    Interactions

    Explore the effect of interactions between explanatory variables. Considering interactions allows for more realistic models that can have better predictive power. You'll also deal with Simpson's Paradox: a non-intuitive result that arises when you have multiple explanatory variables.

  • 3

    Multiple Linear Regression

    See how modeling and linear regression make it easy to work with more than two explanatory variables. Once you've mastered fitting linear regression models, you'll get to implement your own linear regression algorithm.

  • 4

    Multiple Logistic Regression

    Extend your logistic regression skills to multiple explanatory variables. You’ll also learn about logistic distribution, which underpins this form of regression, before implementing your own logistic regression algorithm.

Initializing

Exercise

Confusion matrix

When the response variable has just two outcomes, like the case of churn, the measures of success for the model are "how many cases where the customer churned did the model correctly predict?" and "how many cases where the customer didn't churn did the model correctly predict?". These can be found by generating a confusion matrix and calculating summary metrics on it.

Recall the following definitions:

Accuracy is the proportion of predictions that are correct. $$ \text{accuracy} = \frac{TN + TP}{TN + FN + FP + TP} $$

Sensitivity is the proportion of true observations that are correctly predicted by the model as being true. $$ \text{sensitivity} = \frac{TP}{TP + FN} $$

Specificity is the proportion of false observations that are correctly predicted by the model as being false. $$ \text{specificity} = \frac{TN}{TN + FP} $$

churn and mdl_churn_vs_both_inter are available.

Instructions 1/2

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  • Create the confusion matrix, conf_matrix.