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Course Notes

Use this workspace to take notes, store code snippets, or build your own interactive cheatsheet! The datasets used in this course are available in the datasets folder.

# Import any packages you want to use here
import pandas as pd
from sklearn.model_selection import train_test_split

Take Notes

Add notes here about the concepts you've learned and code cells with code you want to keep.

Train your first classification tree

In this exercise you'll work with the Wisconsin Breast Cancer Dataset from the UCI machine learning repository. You'll predict whether a tumor is malignant or benign based on two features: the mean radius of the tumor (radius_mean) and its mean number of concave points (concave points_mean).

The dataset is already loaded in your workspace and is split into 80% train and 20% test. The feature matrices are assigned to X_train and X_test, while the arrays of labels are assigned to y_train and y_test where class 1 corresponds to a malignant tumor and class 0 corresponds to a benign tumor. To obtain reproducible results, we also defined a variable called SEED which is set to 1.

1 hidden cell

Instructions

  • Import DecisionTreeClassifier from sklearn.tree.
  • Instantiate a DecisionTreeClassifier dt of maximum depth equal to 6.
  • Fit dt to the training set.
  • Predict the test set labels and assign the result to y_pred.
# Import DecisionTreeClassifier from sklearn.tree
from sklearn.tree import DecisionTreeClassifier

# Instantiate a DecisionTreeClassifier 'dt' with a maximum depth of 6
dt = DecisionTreeClassifier(max_depth=6, random_state=SEED)

# Fit dt to the training set
dt.fit(X_train, y_train)

# Predict test set labels
y_pred = dt.predict(X_test)
print(y_pred[0:5])

Awesome! You've just trained your first classification tree! You can see the first five predictions made by the fitted tree on the test set in the console. In the next exercise, you'll evaluate the tree's performance on the entire test set.

Exercise

Evaluate the classification tree

Now that you've fit your first classification tree, it's time to evaluate its performance on the test set. You'll do so using the accuracy metric which corresponds to the fraction of correct predictions made on the test set.

The trained model dt from the previous exercise is loaded in your workspace along with the test set features matrix X_test and the array of labels y_test.

Instructions

  • Import the function accuracy_score from sklearn.metrics.
  • Predict the test set labels and assign the obtained array to y_pred.
  • Evaluate the test set accuracy score of dt by calling accuracy_score() and assign the value to acc.
# Import accuracy_score
from sklearn.metrics import accuracy_score

# Predict test set labels
y_pred = dt.predict(X_test)

# Compute test set accuracy  
acc = accuracy_score(y_test, y_pred)
accuracy_gini = acc
print("Test set accuracy: {:.2f}".format(acc))

Not bad! Using only two features, your tree was able to achieve an accuracy of 89%!

Exercise

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