LR - 10 things to know with detail
- 1. LR stands for Logistic Regression, which is a statistical method used for binary classification tasks in machine learning.
- 2. Logistic Regression is a type of regression analysis where the dependent variable is categorical and the independent variables can be continuous or categorical.
- 3. The logistic function, also known as the sigmoid function, is used in Logistic Regression to map input values to probabilities between 0 and 1.
- 4. The coefficients in Logistic Regression represent the effect of each independent variable on the log-odds of the dependent variable.
- 5. Logistic Regression is commonly used in fields such as finance, marketing, healthcare, and social sciences for tasks such as predicting customer churn, fraud detection, and disease diagnosis.
- 6. Unlike linear regression, Logistic Regression does not assume a linear relationship between the independent variables and the dependent variable.
- 7. Logistic Regression is a parametric model, meaning that it makes assumptions about the underlying distribution of the data.
- 8. The performance of a Logistic Regression model is typically evaluated using metrics such as accuracy, precision, recall, and F1 score.
- 9. Regularization techniques such as L1 (Lasso) and L2 (Ridge) regularization can be used in Logistic Regression to prevent overfitting.
- 10. Logistic Regression is a simple and interpretable model, making it a popular choice for binary classification tasks when the relationship between the independent variables and the dependent variable is not linear.