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.

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 Answer' to see how logistic regression compares to k-NN!

Take Hint (-30 XP)

Exercise

Building a logistic regression model

Instructions