How to Evaluate Logistic Regression Model?
We can evaluate the logistic regression model using the following metrics:
- Accuracy: Accuracy provides the proportion of correctly classified instances.
- Precision: Precision focuses on the accuracy of positive predictions.
- Recall (Sensitivity or True Positive Rate): Recall measures the proportion of correctly predicted positive instances among all actual positive instances.
- F1 Score: F1 score is the harmonic mean of precision and recall.
- Area Under the Receiver Operating Characteristic Curve (AUC-ROC): The ROC curve plots the true positive rate against the false positive rate at various thresholds. AUC-ROC measures the area under this curve, providing an aggregate measure of a model’s performance across different classification thresholds.
- Area Under the Precision-Recall Curve (AUC-PR): Similar to AUC-ROC, AUC-PR measures the area under the precision-recall curve, providing a summary of a model’s performance across different precision-recall trade-offs.
Logistic Regression in Machine Learning
Logistic regression is a supervised machine learning algorithm used for classification tasks where the goal is to predict the probability that an instance belongs to a given class or not. Logistic regression is a statistical algorithm which analyze the relationship between two data factors. The article explores the fundamentals of logistic regression, it’s types and implementations.
Table of Content
- What is Logistic Regression?
- Logistic Function – Sigmoid Function
- Types of Logistic Regression
- Assumptions of Logistic Regression
- How does Logistic Regression work?
- Code Implementation for Logistic Regression
- Precision-Recall Tradeoff in Logistic Regression Threshold Setting
- How to Evaluate Logistic Regression Model?
- Differences Between Linear and Logistic Regression