What is SVM?
Support Vector Machine (SVM) is a supervised learning algorithm commonly used for classification and regression tasks. It works by finding the hyperplane that best separates the data into different classes while maximizing the margin between them. SVM is particularly effective in high-dimensional spaces and is useful when the classes are well-separated. However, it can be computationally intensive, and its performance is highly dependent on the choice of kernel and hyperparameters.
Most Frequent Parameters
-
C: 10, gamma: scale, kernel: linear- Count: 3 -
C: 3, gamma: scale, kernel: linear- Count: 2 -
C: 10, gamma: scale, kernel: rbf- Count: 1 -
C: 5, gamma: scale, kernel: rbf- Count: 1 -
C: 3, gamma: scale, kernel: rbf- Count: 1
Average Scores Based on Model History
- Average Accuracy: 0.90
- Average Precision: 0.90
- Average Recall: 0.90
- Average F1 Score: 0.89
SVM History
General Information
- Model Type: SVM
- Parameters: C: 3, gamma: scale, kernel: linear
- 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
General Information
- Model Type: SVM
- Parameters: C: 3, gamma: scale, kernel: linear
- 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
General Information
- Model Type: SVM
- Parameters: C: 3, gamma: scale, kernel: rbf
- 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: 0.78
- Precision: 0.79
- Recall: 0.78
- F1 Score: 0.77
- Confusion Matrix: [[28, 21, 0], [ 8, 92, 0], [ 1, 5, 5]]
Feature Importance
- No feature importance data available.
File Downloads
General Information
- Model Type: SVM
- Parameters: C: 5, gamma: scale, kernel: rbf
- 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: 0.78
- Precision: 0.82
- Recall: 0.78
- F1 Score: 0.75
- Confusion Matrix: [[101, 1, 0], [ 27, 14, 0], [ 7, 0, 10]]
Feature Importance
- No feature importance data available.
File Downloads
General Information
- Model Type: SVM
- Parameters: C: 10, gamma: scale, kernel: rbf
- 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: 0.79
- Precision: 0.82
- Recall: 0.79
- F1 Score: 0.77
- Confusion Matrix: [[100, 2, 0], [ 23, 17, 1], [ 7, 0, 10]]
Feature Importance
- No feature importance data available.
File Downloads
General Information
- Model Type: SVM
- Parameters: C: 10, gamma: scale, kernel: linear
- 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: 0.90
- Recall: 0.89
- F1 Score: 0.88
- Confusion Matrix: [[115, 0, 0], [ 6, 16, 0], [ 10, 1, 12]]
Feature Importance
- No feature importance data available.
File Downloads
General Information
- Model Type: SVM
- Parameters: C: 10, gamma: scale, kernel: linear
- 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: 0.90
- Recall: 0.89
- F1 Score: 0.88
- Confusion Matrix: [[115, 0, 0], [ 6, 16, 0], [ 10, 1, 12]]
Feature Importance
- No feature importance data available.
File Downloads
General Information
- Model Type: SVM
- Parameters: C: 10, gamma: scale, kernel: linear
- Create Date: October 18, 2024, 2:31 a.m.
- Evaluation Date: October 18, 2024, 2:31 a.m.
- Popularity History Timeframe: 09/17/2024 - 10/17/2024
Performance Metrics
- Accuracy: 0.91
- Precision: 0.91
- Recall: 0.91
- F1 Score: 0.90
- Confusion Matrix: [[114, 1, 0], [ 1, 20, 1], [ 10, 1, 12]]
Feature Importance
- No feature importance data available.
File Downloads
General Information
- Model Type: SVM
- Parameters:
- Create Date: October 17, 2024, 3:38 a.m.
- Evaluation Date: October 17, 2024, 3:38 a.m.
- Popularity History Timeframe: 09/16/2024 - 10/16/2024
Performance Metrics
- Accuracy: 0.94
- Precision: 0.95
- Recall: 0.94
- F1 Score: 0.94
Feature Importance
- No feature importance data available.
File Downloads
General Information
- Model Type: SVM
- Parameters:
- Create Date: October 13, 2024, 8:22 p.m.
- Evaluation Date: October 13, 2024, 8:22 p.m.
- Popularity History Timeframe: 09/13/2024 - 10/13/2024
Performance Metrics
- Accuracy: 0.94
- Precision: 0.95
- Recall: 0.94
- F1 Score: 0.94
Feature Importance
- No feature importance data available.
File Downloads
General Information
- Model Type: SVM
- Parameters:
- Create Date: October 9, 2024, 9:31 a.m.
- Evaluation Date: October 9, 2024, 9:31 a.m.
- Popularity History Timeframe: 09/09/2024 - 10/09/2024
Performance Metrics
- Accuracy: 0.95
- Precision: 0.95
- Recall: 0.95
- F1 Score: 0.95
Feature Importance
- No feature importance data available.
File Downloads
General Information
- Model Type: SVM
- Parameters:
- Create Date: October 7, 2024, 6:54 p.m.
- Evaluation Date: October 7, 2024, 6:54 p.m.
- Popularity History Timeframe: 09/07/2024 - 10/07/2024
Performance Metrics
- Accuracy: 0.92
- Precision: 0.92
- Recall: 0.92
- F1 Score: 0.92
Feature Importance
- No feature importance data available.
File Downloads