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Exercise

Building a logistic regression model

Time to build your first logistic regression model! As Hugo showed in the video, scikit-learn makes it very easy to try different models, since the Train-Test-Split/Instantiate/Fit/Predict paradigm applies to all classifiers and regressors - which are known in scikit-learn as 'estimators'. You'll see this now for yourself as you train a logistic regression model on exactly the same data as in the previous exercise. Will it outperform k-NN? There's only one way to find out!

The feature and target variable arrays X and y have been pre-loaded, and train_test_split has been imported for you from sklearn.model_selection.

Instructions
100 XP
  • Import:
    • LogisticRegression from sklearn.linear_model.
    • confusion_matrix and classification_report from sklearn.metrics.
  • Create training and test sets with 40% (or 0.4) of the data used for testing. Use a random state of 42. This has been done for you.
  • Instantiate a LogisticRegression classifier called logreg.
  • Fit the classifier to the training data and predict the labels of the test set.
  • Compute and print the confusion matrix and classification report. This has been done for you, so hit submit to see how logistic regression compares to k-NN!
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