What are the top feature selection techniques for Gradient Boosting?

1 Filter methods

Filter methods are the simplest and fastest way to select features for gradient boosting. They use statistical measures, such as correlation, chi-square, or information gain, to rank the features according to their relevance to the target variable. Filter methods are independent of the learning algorithm, so they can be applied before training the model. However, they do not consider the interactions between the features, or the effect of the boosting process on the feature importance.

2 Wrapper methods

Wrapper methods are more complex and computationally intensive than filter methods, but they can also produce more accurate and robust feature subsets for gradient boosting. They use the learning algorithm itself to evaluate the features, by applying a search strategy, such as forward, backward, or recursive elimination, to find the optimal combination of features that maximizes the model performance. Wrapper methods can capture the interactions between the features, and the impact of the boosting process on the feature importance, but they are also prone to overfitting and high variance.

3 Embedded methods

Embedded methods are a hybrid of filter and wrapper methods, that integrate the feature selection process within the learning algorithm. They use regularization techniques, such as Lasso, Ridge, or Elastic Net, to penalize the complexity of the model, and shrink the coefficients of the less important features to zero. Embedded methods can achieve a balance between speed and accuracy, and reduce the risk of overfitting and high variance. However, they also require tuning the regularization parameters, and may not work well with categorical or noisy features.

4 Permutation methods

Permutation methods are a recent and innovative way to select features for gradient boosting, that use the concept of feature shuffling to estimate the feature importance. They randomly permute the values of each feature, and measure the change in the model performance. The features that cause a large drop in the performance are considered important, and the ones that cause a small or no change are considered irrelevant. Permutation methods can handle both numerical and categorical features, and are robust to noise and multicollinearity. However, they are also computationally expensive and sensitive to the number of permutations.

5 Shapley values

Shapley values are another novel and powerful way to select features for gradient boosting, that use the concept of cooperative game theory to estimate the feature importance. They measure the contribution of each feature to the model prediction, by averaging over all possible combinations of features. The features that have a high positive or negative impact on the prediction are considered important, and the ones that have a low or zero impact are considered irrelevant. Shapley values can capture the non-linear and interactive effects of the features, and provide a fair and consistent attribution of the feature importance. However, they are also computationally demanding and complex to interpret.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?