The One Quick Stop for ML Models

Machine Learning Models Cheatsheet

This is less of an article and more of a cheatsheet that you can refer to whenever you want; I suggest you save it. There’s a link to a really cool tabular format that has all of this in one place at the end of the article.

This is a really cool picture

1. Linear Regression

Linear regression is a fundamental supervised predictive modeling technique used to model the relationship between a dependent variable and one or more independent variables.

Linear Regression is like trying to fit the perfect trendline to your chaotic life choices — assuming life follows a straight path (spoiler: it doesn’t).

• Assumptions: The model assumes a linear relationship, independence of errors, homoscedasticity (constant variance of errors), no multicollinearity, and normally distributed residuals.
• Distribution: Gaussian distribution.
• Evaluation Metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²).
• Hyperparameter Tuning: Regularization techniques like L1 (Lasso) and L2 (Ridge) help improve model performance.
• Limitations: It is sensitive to outliers and requires independent features.

2. Logistic Regression