Computing precision and recall
The sklearn.metrics submodule has many functions that allow you to easily calculate interesting metrics. So far, you've calculated precision and recall by hand - this is important while you develop your intuition for both these metrics.
In practice, once you do, you can leverage the precision_score and recall_score functions that automatically compute precision and recall, respectively. Both work similarly to other functions in sklearn.metrics - they accept 2 arguments: the first is the actual labels (y_test), and the second is the predicted labels (y_pred).
Let's now try a training size of 90%.
Diese Übung ist Teil des Kurses
Marketing Analytics: Predicting Customer Churn in Python
Interaktive Übung
Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.
# Import train_test_split
from sklearn.model_selection import train_test_split
# Create feature variable
X = telco.drop('Churn', axis=1)
# Create target variable
y = telco['Churn']
# Create training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)
# Import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
# Instantiate the classifier
clf = RandomForestClassifier()
# Fit to the training data
clf.fit(X_train, y_train)
# Predict the labels of the test set
y_pred = clf.predict(X_test)
# Import precision_score