# Regression and Classification

**- Overview**

Regression and classification are both types of supervised machine learning (ML) algorithms. The main difference between them is that regression predicts continuous values, while classification predicts categorical values.

Both regression and classification algorithms are used for prediction and processing of labeled data sets in ML. However, their different approaches to solving ML problems are where they diverge.

Data classification can be based on qualitative, quantitative, geographical, or chronological bases. Some metrics used for evaluating a trained regression model include variance, bias, and error.

The primary objectives regarding the classification of data are: It compresses the volume of data in an easily understandable form such that the similarities and variations can be instantly recognized. It reduces unnecessary details. It promotes comparison and highlights the important aspects of data, including: Confidentiality, Integrity, Availability, Accountability.

Please refer to the following for more information:

- Wikipedia:
**Regression Analysis**. - Wikipedia:
**Correlation Analysis**. - Wikipedia:
**Data Classification**.

**- When To Use Regression and Classification**

We use classification trees when the data set must be divided into categories belonging to the response variables. In most cases, these categories are "yes" or "no." Therefore, there are only two classes, and they are mutually exclusive. Of course, sometimes there may be more than two classes, but in these cases we only use variants of the classification tree algorithm.

However, when we have continuous response variables, we use regression trees. For example, if the response variable is similar to the value of an object or today's temperature, we use a regression tree.

Here are some differences between regression and classification:

- Output variable: Regression algorithms use continuous or real-valued output variables. Classification algorithms use discrete output variables.
- Problem nature: Regression is used to predict a value, while classification is used to separate data into classes.
- Examples: Regression algorithms can be used to predict house prices and weather patterns. Classification algorithms can be used to identify spam emails, detect cancer cells, and perform speech recognition.

**- Regression and Classification Algorithms**

Regression and classification are both types of supervised ML algorithms. The main difference between them is that regression predicts continuous quantities, while classification predicts discrete class labels.

Both regression and classification algorithms work with labeled datasets, but differ in how they are used for different ML problems. Here's some related information about regression and classification algorithms:

- Regression: A supervised ML technique that predicts continuous values. The goal of a regression algorithm is to plot a best-fit line or curve between the data. Some types of regression algorithms include linear, polynomial, logistic, and stepwise.
- Classification: A supervised ML technique that identifies the category of new observations based on training data. A program learns from the given dataset or observations and then classifies new observations into a number of classes or groups.

**- Why Use Machine Learning Models?**

Today, many large organizations use some form of predictive modeling to maximize revenue and drive business growth.

Machine learning (ML) has multiple use cases in different fields. For example, subscription-based platforms like Netflix and Spotify use ML to recommend content based on user activity on the app.

Recommendation systems add direct business value to these companies, as a better user experience will make it more likely that customers will continue to subscribe to the platform. This is an example of an unsupervised ML model.

Likewise, mobile service providers may use ML to analyze user sentiment and curate their products based on market demand. This is an example of a supervised machine learning model.

All ML models can be divided into supervised and unsupervised models. The biggest difference between the two is that supervised algorithms require labeled input and output training data, while unsupervised models can process raw, unlabeled data sets.

Supervised ML models can then be further divided into regression and classification algorithms.

**- The ML Models for both Classification and Regression Problems**

Here are some ML models that can be used for both classification and regression problems:

- Support Vector Machines (SVM): This supervised machine learning technique uses algorithms to classify and train data based on polarity. SVM works by finding a hyperplane that separates different classes or predicts a continuous output value based on the input features.
- Tree-based models: These supervised machine learning algorithms construct a tree-like structure to make predictions. Two commonly used tree-based machine learning models are decision trees and random forests.
- kNN (k-Nearest Neighbors): This simple algorithm stores all available cases and classifies new cases by a majority vote of its k neighbors.

**[More to come ...]**