How is the feature importance of the random forest computed?

How is the feature importance of the random forest computed?

The Random Forest algorithm has built-in feature importance which can be computed in two ways: Gini importance (or mean decrease impurity), which is computed from the Random Forest structure. Let’s look how the Random Forest is constructed. It is a set of Decision Trees.

What happens if you decrease the random forest depth?

If you decrease the maximum depth that the random forest can reach instead of letting the RF to fully grow, what happens to the performance and Overall Accuracy (OA) of the RF? As I have tested the OA decreases. What is the reason? Tree depth determines how flexible the model is. A deeper tree can fit more complicated functions.

How is Gini importance computed in random forest?

Gini importance (or mean decrease impurity), which is computed from the Random Forest structure. Let’s look how the Random Forest is constructed. It is a set of Decision Trees.

What are the advantages and disadvantages of random forest training?

This biggest advantage of this method is a speed of computation – all needed values are computed during the Radom Forest training. The drawbacks of the method is to tendency to prefer (select as important) numerical features and categorical features with high cardinality.

How to interpret mean decrease in accuracy and mean?

“Global” variable importance is the mean decrease of accuracy over all out-of-bag cross validated predictions, when a given variable is permuted after training, but before prediction. “Global” is implicit. Local variable importance is the mean decrease of accuracy by each individual out-of-bag cross validated prediction.

How are feature importances determined in a forest of trees?

Feature importance based on mean decrease in impurity ¶. 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.

How are features shuffled in a forest of trees?

Features are shuffled n times and the model refitted to estimate the importance of it. Please see Permutation feature importance for more details. We can now plot the importance ranking. The same features are detected as most important using both methods. Although the relative importances vary.

How does a random forest algorithm get its name?

As its name suggests, a forest is formed by combining several trees. Similarly, a random forest algorithm combines several machine learning algorithms (Decision trees) to obtain better accuracy. This is also called Ensemble learning.

Why is p-value insignificant in random forest?

Here, the p-value will be insignificant in the case of Random forest as they are non-linear models. Decision trees are highly sensitive to the data they are trained on therefore are prone to Overfitting. However, Random forest leverages this issue and allows each tree to randomly sample from the dataset to obtain different tree structures.

What are the pros and cons of random forest?

Random forest solves the issue of overfitting which occurs in decision trees. One limitation of Random forest is, too many trees can make the processing of the algorithm slow thereby making it ineffective for prediction on real-time data.

How to evaluate the importance of a forest of trees?

This example shows the use of a forest of trees to evaluate the importance of features on an artificial classification task. The blue bars are the feature importances of the forest, along with their inter-trees variability represented by the error bars.

What does negative importance mean in a random forest?

First of all, negative importance, in this case, means that removing a given feature from the model actually improves the performance. So this is nice to see in the case of random, but what is weird is that the highest performance boost can be observed after removing DIS, which was the third most important variable in previous approaches.