https://towardsdatascience.com/regression-an-explanation-of-regression-metrics-and-what-can-go-wrong-a39a9793d914

University of Michigan - Ann Arbor

A regression problem is a type of supervised machine learning task where the objective is to predict an unknown, continuous numerical label based on known input features. It is used to answer 'how much?' or 'how many?' questions. The defining characteristic is that the target labels take on arbitrary numerical values, and the goal of the learning algorithm is to produce a model whose predictions closely approximate these actual observed values.

Regression Problem

Predicting the height of a pediatric patient for a given year based on family trends and input data about the patient's current height/weight data.

Example of A Regression Problem

- Linear regression
- Nonlinear regression

Types of regression

What is the differnce between classification and regression?

https://beginningwithml.wordpress.com/2018/07/02/5-locally-weighted-linear-regression/
Locally weighted linear regression is a general linear regression method, it's also a local regression method. We can know more about local regression by checking its related wiki: https://en.wikipedia.org/wiki/Local_regression

Locally Weighted Linear Regression

A regression model predicts a value $$Y$$ given a set of $$X$$ variables. In the regression model $$Y \sim X$$, we measure the effect of $$X$$ on $$Y$$, i.e., we measure the association between changes in values of $$X$$ and changes in values of $$Y$$. Analyzing a regression model is a method used to solve a regression problem.

Reference: James, G., Witten, D., Hastie, T., & Tibshirani, R. (2017). An introduction to statistical learning: with applications in R. New York: Springer.

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