Pixel importances with a parallel forest of trees#

This example shows the use of a forest of trees to evaluate the impurity based importance of the pixels in an image classification task on the faces dataset. The hotter the pixel, the more important it is.

The code below also illustrates how the construction and the computation of the predictions can be parallelized within multiple jobs.

Loading the data and model fitting#

First, we load the olivetti faces dataset and limit the dataset to contain only the first five classes. Then we train a random forest on the dataset and evaluate the impurity-based feature importance. One drawback of this method is that it cannot be evaluated on a separate test set. For this example, we are interested in representing the information learned from the full dataset. Also, we’ll set the number of cores to use for the tasks.

from sklearn.datasets import fetch_olivetti_faces

We select the number of cores to use to perform parallel fitting of the forest model. -1 means use all available cores.

n_jobs = -1

Load the faces dataset

data = fetch_olivetti_faces()
X, y = data.data, data.target

Limit the dataset to 5 classes.

mask = y < 5
X = X[mask]
y = y[mask]

A random forest classifier will be fitted to compute the feature importances.

from sklearn.ensemble import RandomForestClassifier

forest = RandomForestClassifier(n_estimators=750, n_jobs=n_jobs, random_state=42)

forest.fit(X, y)
RandomForestClassifier(n_estimators=750, n_jobs=-1, random_state=42)
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Feature importance based on mean decrease in impurity (MDI)#

Feature importances are provided by the fitted attribute feature_importances_ and they are computed as the mean and standard deviation of accumulation of the impurity decrease within each tree.


Impurity-based feature importances can be misleading for high cardinality features (many unique values). See Permutation feature importance as an alternative.

import time

import matplotlib.pyplot as plt

start_time = time.time()
img_shape = data.images[0].shape
importances = forest.feature_importances_
elapsed_time = time.time() - start_time

print(f"Elapsed time to compute the importances: {elapsed_time:.3f} seconds")
imp_reshaped = importances.reshape(img_shape)
plt.matshow(imp_reshaped, cmap=plt.cm.hot)
plt.title("Pixel importances using impurity values")
Pixel importances using impurity values
Elapsed time to compute the importances: 0.150 seconds

Can you still recognize a face?

The limitations of MDI is not a problem for this dataset because:

  1. All features are (ordered) numeric and will thus not suffer the cardinality bias

  2. We are only interested to represent knowledge of the forest acquired on the training set.

If these two conditions are not met, it is recommended to instead use the permutation_importance.

Total running time of the script: (0 minutes 1.600 seconds)

Related examples

Feature importances with a forest of trees

Feature importances with a forest of trees

Permutation Importance vs Random Forest Feature Importance (MDI)

Permutation Importance vs Random Forest Feature Importance (MDI)

Permutation Importance with Multicollinear or Correlated Features

Permutation Importance with Multicollinear or Correlated Features

Gradient Boosting regression

Gradient Boosting regression

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