Gradient boosting feature importances
As with random forests, we can extract feature importances from gradient boosting models to understand which features are the best predictors. Sometimes it's nice to try different tree-based models and look at the feature importances from all of them. This can help average out any peculiarities that may arise from one particular model.
The feature importances are stored as a numpy array in the .feature_importances_ property of the gradient boosting model. We'll need to get the sorted indices of the feature importances, using np.argsort(), in order to make a nice plot. We want the features from largest to smallest, so we will use Python's indexing to reverse the sorted importances like feat_importances[::-1].
Este exercício faz parte do curso
Machine Learning for Finance in Python
Instruções do exercício
- Reverse the
sorted_indexvariable to go from greatest to least using python indexing. - Create the sorted feature labels list as
labelsby convertingfeature_namesto a numpy array and indexing withsorted_index. - Create a bar plot of the xticks, and
feature_importancesindexed with thesorted_indexvariable, andlabelsas the xtick labels.
Exercício interativo prático
Experimente este exercício completando este código de exemplo.
# Extract feature importances from the fitted gradient boosting model
feature_importances = gbr.feature_importances_
# Get the indices of the largest to smallest feature importances
sorted_index = np.argsort(feature_importances)[::____]
x = range(features.shape[1])
# Create tick labels
labels = np.array(feature_names)[____]
plt.bar(____, feature_importances[____], tick_label=____)
# Set the tick lables to be the feature names, according to the sorted feature_idx
plt.xticks(rotation=90)
plt.show()