What is LDA?
Linear Discriminant Analysis (LDA) is a classification method that finds a linear combination of features that best separates two or more classes of objects. It is particularly effective when the data follows a Gaussian distribution and is designed to maximize the distance between means of different classes while minimizing the spread within each class. However, LDA's performance may suffer on datasets that do not meet its assumptions of normality and linearity.
Most Frequent Parameters
-
solver: svd- Count: 3 -
priors: None, solver: svd- Count: 3 -
priors: None, solver: lsqr- Count: 1
Average Scores Based on Model History
- Average Accuracy: 0.82
- Average Precision: 0.77
- Average Recall: 0.82
- Average F1 Score: 0.77
LDA Model History
General Information
- Model Type: LDA
- Parameters: priors: None, solver: svd
- 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: 0.93
- Precision: 0.93
- Recall: 0.93
- F1 Score: 0.93
- Confusion Matrix: [[ 6, 6, 0], [ 3, 147, 0], [ 0, 3, 9]]
Feature Importance
- No feature importance data available.
File Downloads
General Information
- Model Type: LDA
- Parameters: priors: None, solver: svd
- 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: 0.94
- Precision: 0.90
- Recall: 0.94
- F1 Score: 0.92
- Confusion Matrix: [[ 0, 7, 0], [ 0, 156, 0], [ 0, 3, 6]]
Feature Importance
- No feature importance data available.
File Downloads
General Information
- Model Type: LDA
- Parameters: priors: None, solver: lsqr
- 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: 0.70
- Precision: 0.72
- Recall: 0.70
- F1 Score: 0.64
- Confusion Matrix: [[10, 39, 0], [ 3, 96, 1], [ 0, 5, 6]]
Feature Importance
- No feature importance data available.
File Downloads
General Information
- Model Type: LDA
- Parameters: priors: None, solver: svd
- 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: 0.69
- Precision: 0.53
- Recall: 0.69
- F1 Score: 0.59
- Confusion Matrix: [[102, 0, 0], [ 40, 0, 1], [ 8, 0, 9]]
Feature Importance
- No feature importance data available.
File Downloads
General Information
- Model Type: LDA
- Parameters: solver: svd
- 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: 0.69
- Precision: 0.53
- Recall: 0.69
- F1 Score: 0.59
- Confusion Matrix: [[102, 0, 0], [ 40, 0, 1], [ 8, 0, 9]]
Feature Importance
- No feature importance data available.
File Downloads
General Information
- Model Type: LDA
- Parameters: solver: svd
- 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: 0.88
- Precision: 0.88
- Recall: 0.88
- F1 Score: 0.86
- Confusion Matrix: [[115, 0, 0], [ 8, 13, 1], [ 9, 2, 12]]
Feature Importance
- No feature importance data available.
File Downloads
General Information
- Model Type: LDA
- Parameters: solver: svd
- 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: 0.88
- Precision: 0.88
- Recall: 0.88
- F1 Score: 0.86
- Confusion Matrix: [[115, 0, 0], [ 8, 13, 1], [ 9, 2, 12]]
Feature Importance
- No feature importance data available.