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Baseline

Evaluating a classifier relative to an appropriate baseline is important. This is especially true for imbalanced datasets, such as ad click-through, because high accuracy can easily be achieved through always selecting the majority class. In this exercise, you will simulate a baseline classifier that always predicts the majority class (non-click) and look at its confusion matrix, as well as what its precision and recall are.

X_train, y_train, X_test, y_test are available in your workspace. pandas as pd, numpy as np, and sklearn are also available in your workspace.

Diese Übung ist Teil des Kurses

Predicting CTR with Machine Learning in Python

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Anleitung zur Übung

  • Create y_pred an array of zeros with the same length as X_test using np.asarray().
  • Print the resulting confusion matrix.
  • Get the precision and recall scores.

Interaktive Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

# Set up baseline predictions
y_pred = np.____([0 for x in range(len(X_test))])

# Look at confusion matrix
print("Confusion matrix: ")
print(____(y_test, y_pred))

# Check precision and recall
prec = ____(y_test, y_pred, average = 'weighted')
recall = ____(y_test, y_pred, average = 'weighted')
print("Precision: %s, Recall: %s" %(prec, recall))
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