CH_03_01 : Types of ML :Supervised Learning

Supervised Learning: Definition & Explanation 

Supervised learning is a type of machine learning where an algorithm learns from labeled training data. The model is trained using input-output pairs, where the input (features) is mapped to the correct output (label). The goal is for the model to learn the underlying pattern and generalize it to new, unseen data.

Supervised learning is commonly used in applications like spam detection, medical diagnosis, sentiment analysis, and stock price prediction.

Types of Supervised Learning

Supervised learning is broadly classified into two types:

1: Regression

2: Classification

1. Regression

Regression is used when the output variable is continuous (numerical). The goal is to predict a value based on input features.

Example:

Imagine a real estate company wants to predict house prices based on factors like area, number of bedrooms, and location. The model is trained on past sales data where house prices are already known. Once trained, it can predict the price of a new house.

Common Algorithms for Regression:

Linear Regression

Polynomial Regression

Decision Tree Regression

Random Forest Regression

Support Vector Regression (SVR)

2. Classification

Classification is used when the output variable is categorical (discrete labels). The goal is to classify new data into one of the predefined categories.

Example:

An email spam filter categorizes incoming emails as either "Spam" or "Not Spam" based on labeled training data. The model learns from past emails, analyzing features like sender, subject line, and content to classify new emails.

Common Algorithms for Classification:

Logistic Regression

Decision Trees

Random Forest

Support Vector Machine (SVM)

k-Nearest Neighbors (k-NN)

Naïve Bayes

Key Differences Between Regression and Classification

Real-World Applications of Supervised Learning