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What is Logistic Regression?

Logistic Regression is a statistical model that predicts the probability of a binary outcome based on one or more predictor variables. It is a widely used method due to its simplicity and interpretability. By estimating the parameters of the logistic function, it can model the relationship between features and the target variable effectively, particularly when the relationship is approximately linear. Despite its limitations in handling non-linear relationships, it remains a strong choice for binary classification tasks, especially with well-scaled features.

Most Frequent Parameters

  • C: 500, fit_intercept: True, intercept_scaling: 1, penalty: l2, solver: liblinear - Count: 4
  • C: 500, penalty: l2, solver: lbfgs - Count: 2
  • C: 500, fit_intercept: False, intercept_scaling: 1, penalty: l2, solver: liblinear - Count: 2
  • C: 10, penalty: l2, solver: lbfgs - Count: 1
  • C: 500, penalty: l2, solver: saga - Count: 1
  • C: 700, fit_intercept: True, intercept_scaling: 1, penalty: l2, solver: liblinear - Count: 1
  • C: 900, fit_intercept: True, intercept_scaling: 1, penalty: l2, solver: liblinear - Count: 1

Average Scores Based on Model History

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

Logistic Regression Model History


General Information
  • Model Type: LogisticRegression
  • Parameters: C: 500, fit_intercept: True, intercept_scaling: 1, penalty: l2, solver: liblinear
  • Create Date: December 20, 2024, 11:03 p.m.
  • Evaluation Date: December 20, 2024, 11:03 p.m.
  • Popularity History Timeframe: 11/21/2024 - 12/21/2024

Performance Metrics
  • Accuracy: 0.94
  • Precision: 0.93
  • Recall: 0.94
  • F1 Score: 0.93
  • Confusion Matrix: [[ 9, 3, 0], [ 2, 146, 2], [ 0, 4, 8]]

Feature Importance
  • No feature importance data available.

File Downloads

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General Information
  • Model Type: LogisticRegression
  • Parameters: C: 500, fit_intercept: True, intercept_scaling: 1, penalty: l2, solver: liblinear
  • Create Date: December 13, 2024, 11:03 p.m.
  • Evaluation Date: December 13, 2024, 11:03 p.m.
  • Popularity History Timeframe: 11/14/2024 - 12/14/2024

Performance Metrics
  • Accuracy: 0.94
  • Precision: 0.93
  • Recall: 0.94
  • F1 Score: 0.93
  • Confusion Matrix: [[ 9, 3, 0], [ 2, 146, 2], [ 0, 4, 8]]

Feature Importance
  • No feature importance data available.

File Downloads

Download CSV Download Model File (pkl) Download README TXT

General Information
  • Model Type: LogisticRegression
  • Parameters: C: 500, fit_intercept: True, intercept_scaling: 1, penalty: l2, solver: liblinear
  • Create Date: December 6, 2024, 11:03 p.m.
  • Evaluation Date: December 6, 2024, 11:03 p.m.
  • Popularity History Timeframe: 11/07/2024 - 12/07/2024

Performance Metrics
  • Accuracy: 0.94
  • Precision: 0.93
  • Recall: 0.94
  • F1 Score: 0.93
  • Confusion Matrix: [[ 9, 3, 0], [ 2, 146, 2], [ 0, 4, 8]]

Feature Importance
  • No feature importance data available.

File Downloads

Download CSV Download Model File (pkl) Download README TXT

General Information
  • Model Type: LogisticRegression
  • Parameters: C: 500, fit_intercept: True, intercept_scaling: 1, penalty: l2, solver: liblinear
  • Create Date: December 3, 2024, 12:09 p.m.
  • Evaluation Date: December 3, 2024, 12:09 p.m.
  • Popularity History Timeframe: 11/03/2024 - 12/03/2024

Performance Metrics
  • Accuracy: 0.94
  • Precision: 0.93
  • Recall: 0.94
  • F1 Score: 0.93
  • Confusion Matrix: [[ 9, 3, 0], [ 2, 146, 2], [ 0, 4, 8]]

Feature Importance
  • No feature importance data available.

File Downloads

Download CSV Download Model File (pkl) Download README TXT

General Information
  • Model Type: LogisticRegression
  • Parameters: C: 900, fit_intercept: True, intercept_scaling: 1, penalty: l2, solver: liblinear
  • Create Date: November 29, 2024, 11:09 p.m.
  • Evaluation Date: November 29, 2024, 11:09 p.m.
  • Popularity History Timeframe: 10/31/2024 - 11/30/2024

Performance Metrics
  • Accuracy: 0.93
  • Precision: 0.93
  • Recall: 0.93
  • F1 Score: 0.92
  • Confusion Matrix: [[ 9, 3, 0], [ 4, 144, 2], [ 0, 4, 8]]

Feature Importance
  • No feature importance data available.

File Downloads

Download CSV Download Model File (pkl) Download README TXT

General Information
  • Model Type: LogisticRegression
  • Parameters: C: 500, fit_intercept: False, intercept_scaling: 1, penalty: l2, solver: liblinear
  • Create Date: November 22, 2024, 11:09 p.m.
  • Evaluation Date: November 22, 2024, 11:09 p.m.
  • Popularity History Timeframe: 10/24/2024 - 11/23/2024

Performance Metrics
  • Accuracy: 0.97
  • Precision: 0.96
  • Recall: 0.97
  • F1 Score: 0.96
  • Confusion Matrix: [[ 5, 2, 0], [ 0, 155, 1], [ 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: LogisticRegression
  • Parameters: C: 500, fit_intercept: False, intercept_scaling: 1, penalty: l2, solver: liblinear
  • Create Date: October 19, 2024, 3:03 p.m.
  • Evaluation Date: October 19, 2024, 3:03 p.m.
  • Popularity History Timeframe: 09/19/2024 - 10/19/2024

Performance Metrics
  • Accuracy: 0.69
  • Precision: 0.72
  • Recall: 0.69
  • F1 Score: 0.65
  • Confusion Matrix: [[13, 36, 0], [ 3, 92, 5], [ 0, 5, 6]]

Feature Importance
  • No feature importance data available.

File Downloads

Download CSV Download Model File (pkl) Download README TXT

General Information
  • Model Type: LogisticRegression
  • Parameters: C: 700, fit_intercept: True, intercept_scaling: 1, penalty: l2, solver: liblinear
  • Create Date: October 18, 2024, 1:16 a.m.
  • Evaluation Date: October 18, 2024, 1:16 a.m.
  • Popularity History Timeframe: 09/18/2024 - 10/18/2024

Performance Metrics
  • Accuracy: 0.73
  • Precision: 0.69
  • Recall: 0.73
  • F1 Score: 0.70
  • Confusion Matrix: [[93, 9, 0], [25, 11, 5], [ 2, 3, 12]]

Feature Importance
  • No feature importance data available.

File Downloads

Download CSV Download Model File (pkl) Download README TXT

General Information
  • Model Type: LogisticRegression
  • Parameters: C: 500, penalty: l2, solver: saga
  • Create Date: October 17, 2024, 11:39 p.m.
  • Evaluation Date: October 17, 2024, 11:39 p.m.
  • Popularity History Timeframe: 09/18/2024 - 10/18/2024

Performance Metrics
  • Accuracy: 0.54
  • Precision: 0.61
  • Recall: 0.54
  • F1 Score: 0.56
  • Confusion Matrix: [[57, 43, 2], [18, 17, 6], [ 1, 3, 13]]

Feature Importance
  • No feature importance data available.

File Downloads

Download CSV Download Model File (pkl) Download README TXT

General Information
  • Model Type: LogisticRegression
  • Parameters: C: 500, penalty: l2, solver: lbfgs
  • Create Date: October 17, 2024, 11:31 p.m.
  • Evaluation Date: October 17, 2024, 11:31 p.m.
  • Popularity History Timeframe: 09/18/2024 - 10/18/2024

Performance Metrics
  • Accuracy: 0.98
  • Precision: 0.98
  • Recall: 0.98
  • F1 Score: 0.97
  • Confusion Matrix: [[114, 1, 0], [ 1, 21, 0], [ 1, 1, 21]]

Feature Importance
  • No feature importance data available.

File Downloads

Download CSV Download Model File (pkl) Download README TXT

General Information
  • Model Type: LogisticRegression
  • Parameters: C: 500, penalty: l2, solver: lbfgs
  • Create Date: October 17, 2024, 7:15 p.m.
  • Evaluation Date: October 17, 2024, 7:15 p.m.
  • Popularity History Timeframe: 09/17/2024 - 10/17/2024

Performance Metrics
  • Accuracy: 0.98
  • Precision: 0.98
  • Recall: 0.98
  • F1 Score: 0.97
  • Confusion Matrix: [[114, 1, 0], [ 1, 21, 0], [ 1, 1, 21]]

Feature Importance
  • No feature importance data available.

File Downloads

Download CSV Download Model File (pkl) Download README TXT

General Information
  • Model Type: LogisticRegression
  • Parameters: C: 10, penalty: l2, solver: lbfgs
  • Create Date: October 17, 2024, 5:31 p.m.
  • Evaluation Date: October 17, 2024, 5:31 p.m.
  • Popularity History Timeframe: None

Performance Metrics
  • Accuracy: 0.91
  • Precision: 0.90
  • Recall: 0.91
  • F1 Score: 0.90
  • Confusion Matrix: [[111, 1, 3], [ 1, 20, 1], [ 8, 1, 14]]

Feature Importance
  • No feature importance data available.

File Downloads

Download CSV Download Model File (pkl) Download README TXT

General Information
  • Model Type: LogisticRegression
  • Parameters: None
  • Create Date: October 16, 2024, 6:38 p.m.
  • Evaluation Date: October 16, 2024, 6:38 p.m.
  • Popularity History Timeframe: None

Performance Metrics
  • Accuracy: 0.85
  • Precision: 0.89
  • Recall: 0.85
  • F1 Score: 0.86

Feature Importance
  • No feature importance data available.

File Downloads

General Information
  • Model Type: LogisticRegression
  • Parameters: None
  • Create Date: October 13, 2024, 11:22 a.m.
  • Evaluation Date: October 13, 2024, 11:22 a.m.
  • Popularity History Timeframe: None

Performance Metrics
  • Accuracy: 0.85
  • Precision: 0.89
  • Recall: 0.85
  • F1 Score: 0.86

Feature Importance
  • No feature importance data available.

File Downloads

General Information
  • Model Type: LogisticRegression
  • Parameters: None
  • Create Date: October 9, 2024, 12:31 a.m.
  • Evaluation Date: October 9, 2024, 12:31 a.m.
  • Popularity History Timeframe: None

Performance Metrics
  • Accuracy: 0.96
  • Precision: 0.96
  • Recall: 0.96
  • F1 Score: 0.96

Feature Importance
  • No feature importance data available.

File Downloads

General Information
  • Model Type: LogisticRegression
  • Parameters: None
  • Create Date: October 7, 2024, 9:54 a.m.
  • Evaluation Date: October 7, 2024, 9:54 a.m.
  • Popularity History Timeframe: None

Performance Metrics
  • Accuracy: 0.94
  • Precision: 0.94
  • Recall: 0.94
  • F1 Score: 0.93

Feature Importance
  • No feature importance data available.

File Downloads