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Improving the plot

In order to make the plot more readable, we need to do achieve two goals:

  • Re-order the bars in ascending order.
  • Add labels to the plot that correspond to the feature names.

To do this, we'll take advantage of NumPy indexing. The .argsort() method sorts an array and returns the indices. We'll use these indices to achieve both goals.

This exercise is part of the course

Marketing Analytics: Predicting Customer Churn in Python

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Exercise instructions

  • Calculate the sorted indices of importances by using np.argsort() on importances.
  • Create the sorted labels by accessing the columns of X and indexing by sorted_index.
  • Create the plot by indexing importances using sorted_index and specifying the keyword argument tick_label=labels.

Hands-on interactive exercise

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

# Sort importances
sorted_index = ____(____)

# Create labels
labels = X.columns[____]

# Clear current plot
plt.clf()

# Create plot
plt.barh(range(X.shape[1]), importances[____], tick_label=____)
plt.show()
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