Decision trees

Your task in this exercise is to make a simple decision tree using scikit-learn's DecisionTreeClassifier on the breast cancer dataset that comes pre-loaded with scikit-learn.

This dataset contains numeric measurements of various dimensions of individual tumors (such as perimeter and texture) from breast biopsies and a single outcome value (the tumor is either malignant, or benign).

We've preloaded the dataset of samples (measurements) into X and the target values per tumor into y. Now, you have to split the complete dataset into training and testing sets, and then train a DecisionTreeClassifier. You'll specify a parameter called max_depth. Many other parameters can be modified within this model, and you can check all of them out here.

This exercise is part of the course

Extreme Gradient Boosting with XGBoost

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Exercise instructions

Import:
- train_test_split from sklearn.model_selection.
- DecisionTreeClassifier from sklearn.tree.
Create training and test sets such that 20% of the data is used for testing. Use a random_state of 123.
Instantiate a DecisionTreeClassifier called dt_clf_4 with a max_depth of 4. This parameter specifies the maximum number of successive split points you can have before reaching a leaf node.
Fit the classifier to the training set and predict the labels of the test set.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Import the necessary modules
____
____

# Create the training and test sets
X_train, X_test, y_train, y_test = ____(____, ____, test_size=____, random_state=____)

# Instantiate the classifier: dt_clf_4
dt_clf_4 = ____

# Fit the classifier to the training set
____

# Predict the labels of the test set: y_pred_4
y_pred_4 = ____

# Compute the accuracy of the predictions: accuracy
accuracy = float(np.sum(y_pred_4==y_test))/y_test.shape[0]
print("accuracy:", accuracy)

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