Logistic regression vs classification tree

A classification tree divides the feature space into rectangular regions. In contrast, a linear model such as logistic regression produces only a single linear decision boundary dividing the feature space into two decision regions.

We have written a custom function called plot_labeled_decision_regions() that you can use to plot the decision regions of a list containing two trained classifiers. You can type help(plot_labeled_decision_regions) in the IPython shell to learn more about this function.

X_train, X_test, y_train, y_test, the model dt that you've trained in an earlier exercise , as well as the function plot_labeled_decision_regions() are available in your workspace.

Import LogisticRegression from sklearn.linear_model.
Instantiate a LogisticRegression model and assign it to logreg.
Fit logreg to the training set.
Review the plot generated by plot_labeled_decision_regions().

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Exercise

Logistic regression vs classification tree

Instructions