Now that we have taken a look at what is going on under the hood, we are ready to implement Gradient Boosting 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 a Gradient Boosted Trees classification model using GradientBoostingClassifier() with the n_estimators set to 15. Leave all other parameters to their default values. Store the model in a variable named grad_classifier.

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


Fit grad_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 Gradient Boosted Trees 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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