Finally, we will calculate the precision and recall for the model that we created in the previous step.
First, we will import precision_score and recall_score from Scikit-learn:
from sklearn.metrics import precision_score, << your code goes here >>
Put the test set through the pipeline we created:
X_test_transformed = << your code goes here >>.transform(X_test)
Fit our model on the test set:
log_clf = LogisticRegression(solver="lbfgs", random_state=42)
log_clf.fit(X_train_transformed, y_train)
Make our predictions using predict on the transformed test dataset:
y_pred = log_clf.<< your code goes here >>(X_test_transformed)
Finally, we will calculate and print the Precision and Recall scores of our model:
print("Precision: {:.2f}%".format(100 * precision_score(y_test, y_pred)))
print("Recall: {:.2f}%".format(100 * recall_score(y_test, y_pred)))
Note - Having trouble with the assessment engine? Follow the steps listed here
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