What is ExtraTrees?

The Extra Trees Classifier is another ensemble learning method similar to Random Forest. It builds a large number of unpruned decision trees and uses a random subset of features for splitting at each node, which enhances the diversity among the trees. This algorithm typically results in better generalization and robustness against overfitting compared to traditional decision trees. Extra Trees is efficient for handling large datasets and complex relationships, making it a valuable tool for classification tasks.

Most Frequent Parameters

• bootstrap: True, criterion: entropy, max_features: sqrt, min_samples_leaf: 1, n_estimators: 100 - Count: 4
• max_depth: 20, min_samples_split: 5, n_estimators: 100 - Count: 2
• bootstrap: True, criterion: gini, max_features: sqrt, min_samples_leaf: 1, n_estimators: 200 - Count: 2
• bootstrap: False, criterion: gini, max_features: sqrt, min_samples_leaf: 1, n_estimators: 200 - Count: 2
• max_depth: 30, min_samples_split: 2, n_estimators: 100 - Count: 1

Average Scores Based on Model History

• Average Accuracy: 0.91
• Average Precision: 0.92
• Average Recall: 0.91
• Average F1 Score: 0.90