Wow, that was a lot to take in! Let’s take this opportunity to implement AdaBoost on a real dataset and solve a classification problem.

We will be using a dataset from UCI’s Machine Learning Repository to evaluate the acceptability of a car based on a set of features that encompasses their price and technical characteristics.



Create the base estimator for the AdaBoost classifier in the form a decision stump using DecisionTreeClassifier() and store it in a variable named decision_stump. Recall, that a decision stump is a decision tree with only two leaf nodes.

Print the parameters of the decision stump using the .get_params() method.


Create an AdaBoost classification model with the base_estimator parameter set to decision_stump and n_estimators set to 5. Store the model in a variable named ada_classifier.

Print the parameters of the AdaBoost model using the .get_params() method.


Fit ada_classifier using the training features (X_train) and corresponding labels (y_train).

Predict the classes of the testing dataset (X_test) and store them as an array in a variable named y_pred.


Now we will explore some of the most common evaluation metrics for classification on our trained AdaBoost model.

  • Calculate the accuracy and store it in a variable named accuracy.
  • Calculate the precision and store it in a variable named precision.
  • Calculate the recall and store it in a variable named recall.
  • Calculate the f1-score and store it in a variable named f1.

Remove the comments from the code block to print the evaluation metrics you just stored.


Take a look at the confusion matrix by removing the comments in the following code block.

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