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

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

Regression Metrics

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

Regression models predict 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.

Regression Models

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.

University of Michigan - Ann Arbor

Claude

- Classification Problems
- Regression Problems

Types of supervised learning problems

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, pp. 3-7). New York: springer.

An Introduction to Statistical Learning with Applications in R

Dive into Deep Learning

Classification problems involve predicting a categorical label for a given input. The model's output for a classification task is typically a probability distribution over the set of possible labels, indicating the likelihood of the input belonging to each category.

Classification problem in statistical learning

Regression Problem

Supervised Learning has been broadly classified into 2 types.
1. Regression
2. Classification

The possible applications of supervised learning areas:

Supervised Learning Algorithms are used in a variety of applications. Let’s go through some of the most well-known applications.

BioInformatics –recognition and classification using stored of Biological Information of humans such as fingerprints, iris texture, earlobe and so on.

Speech Recognition – recognition of voice using classification and regression. 

Spam Detection – applications used where the unreal or computer-based messages and E-Mails are to be blocked. 

Object-Recognition for Vision –  identify something(object) usng image resources such as picture or video

(https://www.edureka.co/blog/supervised-learning/)

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