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Exercise

Visualizing classification model performance

In this exercise, you will be solving a classification problem where the "popularity" column in the music_df dataset has been converted to binary values, with 1 representing popularity more than or equal to the median for the "popularity" column, and 0 indicating popularity below the median.

Your task is to build and visualize the results of three different models to classify whether a song is popular or not.

The data has been split, scaled, and preloaded for you as X_train_scaled, X_test_scaled, y_train, and y_test. Additionally, KNeighborsClassifier, DecisionTreeClassifier, and LogisticRegression have been imported.

Instructions

100 XP
  • Create a dictionary of "Logistic Regression", "KNN", and "Decision Tree Classifier", setting the dictionary's values to a call of each model.
  • Loop through the values in models.
  • Instantiate a KFold object to perform 6 splits, setting shuffle to True and random_state to 12.
  • Perform cross-validation using the model, the scaled training features, the target training set, and setting cv equal to kf.