What is Classification and Regression in Supervised Machine Learning?

 

The topic is classification and regression, which are types of supervised machine learning. In supervised machine learning, we have labeled data, meaning the input data is mapped with the correct output, known as the label.

We come across two types of problems in supervised machine learning:

1. Classification
2. Regression

Classification

Classification is a type of machine learning where the output (label) is a categorical value. To better understand classification, consider the example of heart disease data. In the heart patient data, if a patient has a heart problem, they will be labeled as a patient, whereas a patient with no heart problem will be classified as Healthy.

Classification can involve two categories or multiple categories, which is known as a multi-class classification problem. For example, we can categorize patient conditions from Healthy to Critical:

1. Healthy
2. Minor Problem
3. Critical

In this classification of heart data, Healthy, Minor Problem, and Critical are the classes (categories).

So, in classification, the machine learns from the input and output, and upon feeding new data, it classifies the new data based on its learning as Healthy or Critical.

Other examples of classification include:

1. Email Spam Classification: Classifying an email as spam or not.
2. Customer Churn: Determining if a customer will churn or not.
3. Iris Flower Classification: The famous example of classifying iris flowers into different species.

We have different kinds of algorithms that deal with classification problems. I’ll write a blog on each of these algorithms:

1. Logistic Regression
2. Support Vector Machines
3. Decision Trees
4. Random Forest
5. Naive Bayes
6. K-Nearest Neighbors (KNN) models

Regression

Regression is another type of supervised machine learning where the output is a continuous value, such as price (PKR, DOLLAR) or weight. Regression is used when the data output is numerical, like predicting house prices. The dataset has different inputs with corresponding outputs as the house price. The machine learns from the data, and upon receiving new input values, it predicts the house price.

Another common example of a regression problem is stock price prediction or cryptocurrency price prediction.

Some common regression algorithms are:

1. Linear Regression
2. Polynomial Regression
3. Support Vector Machine Regression
4. Decision Tree Regression

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