Exercise

# Logistic Regression

In this last lesson you'll **combine three algorithms** into one model with the **VotingClassifier**. This allows us to benefit from the different aspects from all models, and hopefully improve overall performance and detect more fraud. The first model, the Logistic Regression, has a slightly higher recall score than our optimal Random Forest model, but gives a lot more false positives. You'll also add a Decision Tree with balanced weights to it. The data is already split into a training and test set, i.e. `X_train`

, `y_train`

, `X_test`

, `y_test`

are available.

In order to understand how the Voting Classifier can potentially improve your original model, you should check the standalone results of the Logistic Regression model first.

Instructions

**100 XP**

- Define a LogisticRegression model with class weights that are 1:15 for the fraud cases.
- Fit the model to the training set, and obtain the model predictions.
- Print the classification report and confusion matrix.