README.md

Regression

Regression in machine learning consists of mathematical methods that allow data scientists to predict a continuous outcome (y) based on the value of one or more predictor variables (x). Linear regression is probably the most popular form of regression analysis because of its ease-of-use in predicting and forecasting

Imagine you're car shopping and have decided that gas mileage is a deciding factor in your decision to buy. If you wanted to predict the miles per gallon of some promising rides, how would you do it? Well, since you know the different features of the car (weight, horsepower, displacement, etc.) one possible method is regression. By plotting the average MPG of each car given its features you can then use regression techniques to find the relationship of the MPG and the input features. The regression function here could be represented as $Y = f(X)$, where Y would be the MPG and X would be the input features like the weight, displacement, horsepower, etc. The target function is $f$ and this curve helps us predict whether it’s beneficial to buy or not buy. This mechanism is called regression.

Subsets of Regression:

best way is to learn is from TOP to BOTTOM.

• Linear Regression
• Polynomial Regression
• Ridge Regression
• Lasso Regression
• Elastic Net Regression
• Logistic Regression
• Cox Regression
• Poisson Regression
• Ordinal Regression
• Support Vector Regression
• Principal Components Regression
• Partial Least Squares Regression
• Negative Binomial Regression
• Quantile Regression

Informative Resources:

• https://machinelearningmastery.com/types-of-classification-in-machine-learning/
• https://builtin.com/data-science/regression-machine-learning (use incognito)
• https://square.github.io/pysurvival/
• https://machinelearningmastery.com/classification-versus-regression-in-machine-learning/
• https://www.geeksforgeeks.org/regression-classification-supervised-machine-learning/