100 Facts About Regression Models

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Here’s a list of 100 facts about regression models:

1. Regression models are a type of supervised learning algorithm.
2. They are used for predicting a continuous target variable.
3. Common types of regression models include linear regression, polynomial regression, ridge regression, and lasso regression.
4. Regression models are trained on a labeled dataset, where each example has a known numerical target value.
5. The output of a regression model is a continuous numerical value.
6. Evaluation metrics for regression models include Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared.
7. MSE penalizes large errors more than MAE, making it sensitive to outliers.
8. R-squared measures the proportion of the variance in the target variable that is predictable from the independent variables.
9. Residuals are the differences between the predicted and actual values in regression models.
10. Heteroscedasticity refers to the situation where the variability of residuals is not constant across all levels of the independent variable.
11. Multicollinearity occurs when independent variables in a regression model are highly correlated.