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

  • 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: 0.89
  • Average Precision: 0.89
  • Average Recall: 0.89
  • Average F1 Score: 0.88

ExtraTrees Model History


General Information
  • Model Type: ExtraTrees
  • Parameters: bootstrap: False, criterion: gini, max_features: sqrt, min_samples_leaf: 1, n_estimators: 200
  • Create Date: November 30, 2024, 9:09 a.m.
  • Evaluation Date: November 30, 2024, 9:09 a.m.
  • Popularity History Timeframe: 10/30/2024 - 11/29/2024

Performance Metrics
  • Accuracy: 0.97
  • Precision: 0.97
  • Recall: 0.97
  • F1 Score: 0.96
  • Confusion Matrix: [[ 9, 3, 0], [ 0, 150, 0], [ 0, 3, 9]]

Feature Importance
  • No feature importance data available.

File Downloads

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General Information
  • Model Type: ExtraTrees
  • Parameters: bootstrap: False, criterion: gini, max_features: sqrt, min_samples_leaf: 1, n_estimators: 200
  • Create Date: November 23, 2024, 9:09 a.m.
  • Evaluation Date: November 23, 2024, 9:09 a.m.
  • Popularity History Timeframe: 10/23/2024 - 11/22/2024

Performance Metrics
  • Accuracy: 0.97
  • Precision: 0.97
  • Recall: 0.97
  • F1 Score: 0.97
  • Confusion Matrix: [[ 5, 2, 0], [ 0, 156, 0], [ 0, 3, 6]]

Feature Importance
  • No feature importance data available.

File Downloads

Download CSV Download Model File (pkl) Download README TXT

General Information
  • Model Type: ExtraTrees
  • Parameters: bootstrap: True, criterion: gini, max_features: sqrt, min_samples_leaf: 1, n_estimators: 200
  • Create Date: October 20, 2024, 12:03 a.m.
  • Evaluation Date: October 20, 2024, 12:03 a.m.
  • Popularity History Timeframe: 09/19/2024 - 10/19/2024

Performance Metrics
  • Accuracy: 0.83
  • Precision: 0.84
  • Recall: 0.83
  • F1 Score: 0.82
  • Confusion Matrix: [[33, 16, 0], [ 6, 94, 0], [ 2, 3, 6]]

Feature Importance
  • No feature importance data available.

File Downloads

Download CSV Download Model File (pkl) Download README TXT

General Information
  • Model Type: ExtraTrees
  • Parameters: bootstrap: True, criterion: gini, max_features: sqrt, min_samples_leaf: 1, n_estimators: 200
  • Create Date: October 18, 2024, 10:16 a.m.
  • Evaluation Date: October 18, 2024, 10:16 a.m.
  • Popularity History Timeframe: 09/18/2024 - 10/18/2024

Performance Metrics
  • Accuracy: 0.82
  • Precision: 0.84
  • Recall: 0.82
  • F1 Score: 0.80
  • Confusion Matrix: [[100, 2, 0], [ 20, 21, 0], [ 7, 0, 10]]

Feature Importance
  • No feature importance data available.

File Downloads

Download CSV Download Model File (pkl) Download README TXT

General Information
  • Model Type: ExtraTrees
  • Parameters: max_depth: 30, min_samples_split: 2, n_estimators: 100
  • Create Date: October 18, 2024, 8:39 a.m.
  • Evaluation Date: October 18, 2024, 8:39 a.m.
  • Popularity History Timeframe: 09/17/2024 - 10/17/2024

Performance Metrics
  • Accuracy: 0.84
  • Precision: 0.86
  • Recall: 0.84
  • F1 Score: 0.83
  • Confusion Matrix: [[100, 2, 0], [ 16, 25, 0], [ 7, 0, 10]]

Feature Importance
  • No feature importance data available.

File Downloads

Download CSV Download Model File (pkl) Download README TXT

General Information
  • Model Type: ExtraTrees
  • Parameters: max_depth: 20, min_samples_split: 5, n_estimators: 100
  • Create Date: October 18, 2024, 8:31 a.m.
  • Evaluation Date: October 18, 2024, 8:31 a.m.
  • Popularity History Timeframe: 09/17/2024 - 10/17/2024

Performance Metrics
  • Accuracy: 0.89
  • Precision: 0.89
  • Recall: 0.89
  • F1 Score: 0.88
  • Confusion Matrix: [[114, 0, 1], [ 4, 16, 2], [ 11, 0, 12]]

Feature Importance
  • No feature importance data available.

File Downloads

Download CSV Download Model File (pkl) Download README TXT

General Information
  • Model Type: ExtraTrees
  • Parameters: max_depth: 20, min_samples_split: 5, n_estimators: 100
  • Create Date: October 18, 2024, 4:16 a.m.
  • Evaluation Date: October 18, 2024, 4:16 a.m.
  • Popularity History Timeframe: 09/17/2024 - 10/17/2024

Performance Metrics
  • Accuracy: 0.89
  • Precision: 0.89
  • Recall: 0.89
  • F1 Score: 0.88
  • Confusion Matrix: [[114, 0, 1], [ 4, 16, 2], [ 11, 0, 12]]

Feature Importance
  • No feature importance data available.

File Downloads

Download CSV Download Model File (pkl) Download README TXT