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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.

Cet exercice fait partie du cours

Marketing Analytics: Predicting Customer Churn in Python

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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.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de 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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