In this post, I’ll discuss random forests, another popular approach for feature ranking.
Random forests are among the most popular machine learning methods thanks to their relatively good accuracy, robustness and ease of use. They also provide two straightforward methods for feature selection: mean decrease impurity and mean decrease accuracy.
Mean decrease impurity
Random forest consists of a number of decision trees. Every node in the decision trees is a condition on a single feature, designed to split the dataset into two so that similar response values end up in the same set. The measure based on which the (locally) optimal condition is chosen is called impurity. For classification, it is typically either Gini impurity or information gain/entropy and for regression trees it is variance. Thus when training a tree, it can be computed how much each feature decreases the weighted impurity in a tree. For a forest, the impurity decrease from each feature can be averaged and the features are ranked according to this measure.
13; from sklearn.datasets import load_boston13; from sklearn.ensemble import RandomForestRegressor13; import numpy as np13; #Load boston housing dataset as an example13; boston = load_boston()13; X = boston["data"]13; Y = boston["target"]13; names = boston["feature_names"]13; rf = RandomForestRegressor()13; rf.fit(X, Y)13; print "Features sorted by their score:"13; print sorted(zip(map(lambda x: round(x, 4), rf.feature_importances_), names), 13; reverse=True)13;
Features sorted by their score:
[(0.5298, 'LSTAT'), (0.4116, 'RM'), (0.0252, 'DIS'), (0.0172, 'CRIM'), (0.0065, 'NOX'), (0.0035, 'PTRATIO'), (0.0021, 'TAX'), (0.0017, 'AGE'), (0.0012, 'B'), (0.0008, 'INDUS'), (0.0004, 'RAD'), (0.0001, 'CHAS'), (0.0, 'ZN')]
There are a few things to keep in mind when using the impurity based ranking. Firstly, feature selection based on impurity reduction is biased towards preferring variables with more categories (see Bias in random forest variable importance measures). Secondly, when the dataset has two (or more) correlated features, then from the point of view of the model, any of these correlated features can be used as the predictor, with no concrete preference of one over the others. But once one of them is used, the importance of others is significantly reduced since effectively the impurity they can remove is already removed by the first feature. As a consequence, they will have a lower reported importance. This is not an issue when we want to use feature selection to reduce overfitting, since it makes sense to remove features that are mostly duplicated by other features. But when interpreting the data, it can lead to the incorrect conclusion that one of the variables is a strong predictor while the others in the same group are unimportant, while actually they are very close in terms of their relationship with the response variable.
The effect of this phenomenon is somewhat reduced thanks to random selection of features at each node creation, but in general the effect is not removed completely. In the following example, we have three correlated variables (X_0, X_1, X_2), and no noise in the data, with the output variable simply being the sum of the three features:
size = 10000 np.random.seed(seed=10) X_seed = np.random.normal(0, 1, size) X0 = X_seed + np.random.normal(0, .1, size) X1 = X_seed + np.random.normal(0, .1, size) X2 = X_seed + np.random.normal(0, .1, size) X = np.array([X0, X1, X2]).T Y = X0 + X1 + X2 rf = RandomForestRegressor(n_estimators=20, max_features=2) rf.fit(X, Y); print "Scores for X0, X1, X2:", map(lambda x:round (x,3), rf.feature_importances_)
Scores for X0, X1, X2: [0.278, 0.66, 0.062]
When we compute the feature importances, we see that (X_1) is computed to have over 10x higher importance than (X_2), while their “true” importance is very similar. This happens despite the fact that the data is noiseless, we use 20 trees, random selection of features (at each split, only two of the three features are considered) and a sufficiently large dataset.
One thing to point out though is that the difficulty of interpreting the importance/ranking of correlated variables is not random forest specific, but applies to most model based feature selection methods.
Mean decrease accuracy
Another popular feature selection method is to directly measure the impact of each feature on accuracy of the model. The general idea is to permute the values of each feature and measure how much the permutation decreases the accuracy of the model. Clearly, for unimportant variables, the permutation should have little to no effect on model accuracy, while permuting important variables should significantly decrease it.
This method is not directly exposed in sklearn, but it is straightforward to implement it. Continuing from the previous example of ranking the features in the Boston housing dataset:
from sklearn.cross_validation import ShuffleSplit from sklearn.metrics import r2_score from collections import defaultdict X = boston["data"] Y = boston["target"] rf = RandomForestRegressor() scores = defaultdict(list) #crossvalidate the scores on a number of different random splits of the data for train_idx, test_idx in ShuffleSplit(len(X), 100, .3): X_train, X_test = X[train_idx], X[test_idx] Y_train, Y_test = Y[train_idx], Y[test_idx] r = rf.fit(X_train, Y_train) acc = r2_score(Y_test, rf.predict(X_test)) for i in range(X.shape): X_t = X_test.copy() np.random.shuffle(X_t[:, i]) shuff_acc = r2_score(Y_test, rf.predict(X_t)) scores[names[i]].append((acc-shuff_acc)/acc) print "Features sorted by their score:" print sorted([(round(np.mean(score), 4), feat) for feat, score in scores.items()], reverse=True)
Features sorted by their score:
[(0.7276, 'LSTAT'), (0.5675, 'RM'), (0.0867, 'DIS'), (0.0407, 'NOX'), (0.0351, 'CRIM'), (0.0233, 'PTRATIO'), (0.0168, 'TAX'), (0.0122, 'AGE'), (0.005, 'B'), (0.0048, 'INDUS'), (0.0043, 'RAD'), (0.0004, 'ZN'), (0.0001, 'CHAS')]
In this example
RM are two features that strongly impact model performance: permuting them decreases model performance by ~73% and ~57% respectively. Keep in mind though that these measurements are made only after the model has been trained (and is depending) on all of these features. This doesn’t mean that if we train the model without one these feature, the model performance will drop by that amount, since other, correlated features can be used instead.
Random forests are a popular method for feature ranking, since they are so easy to apply: in general they require very little feature engineering and parameter tuning and mean decrease impurity is exposed in most random forest libraries. But they come with their own gotchas, especially when data interpretation is concerned. With correlated features, strong features can end up with low scores and the method can be biased towards variables with many categories. As long as the gotchas are kept in mind, there really is no reason not to try them out on your data.
Next up: Stability selection, recursive feature elimination, and an example comparing all discussed methods side by side.