Get startedGet started for free

Train/test split + computing accuracy

It's time to practice splitting your data into training and test sets with the churn_df dataset!

NumPy arrays have been created for you containing the features as X and the target variable as y.

This exercise is part of the course

Supervised Learning with scikit-learn

View Course

Exercise instructions

  • Import train_test_split from sklearn.model_selection.
  • Split X and y into training and test sets, setting test_size equal to 20%, random_state to 42, and ensuring the target label proportions reflect that of the original dataset.
  • Fit the knn model to the training data.
  • Compute and print the model's accuracy for the test data.

Hands-on interactive exercise

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

# Import the module
from ____ import ____

X = churn_df.drop("churn", axis=1).values
y = churn_df["churn"].values

# Split into training and test sets
X_train, X_test, y_train, y_test = ____(____, ____, test_size=____, random_state=____, stratify=____)
knn = KNeighborsClassifier(n_neighbors=5)

# Fit the classifier to the training data
____

# Print the accuracy
print(knn.score(____, ____))
Edit and Run Code