Supervised Learning

Introduction

Supervised Learning is a type of machine learning where the model learns from labeled data.

Definition

Supervised Learning is a learning technique in which input data is paired with correct output labels, and the model learns to map inputs to outputs.

Types of Supervised Learning

1. Regression

Used when output is continuous. Example: house price prediction.

2. Classification

Used when output is categorical. Example: spam detection.

Supervised Learning Process

 Labeled Data → Train Model → Evaluate → Predict 

Example: Classification

 from sklearn.tree import DecisionTreeClassifier X = [[0, 0], [1, 1]] y = [0, 1] model = DecisionTreeClassifier() model.fit(X, y) print(model.predict([[2, 2]])) 

Real Life Use Cases

• Email spam detection
• Credit risk prediction
• Medical diagnosis
• Customer churn prediction

Advantages

• High accuracy with quality labeled data
• Clear performance metrics

Disadvantages

• Requires labeled data
• Labeling can be expensive

Interview Questions with Answers

1. What is supervised learning?
   Answer: It is a learning method where models are trained using labeled data.
2. Difference between regression and classification?
   Answer: Regression predicts continuous values while classification predicts categories.
3. Give real world example of supervised learning.
   Answer: Spam detection where emails are labeled as spam or not spam.

Certification Practice Questions with Answers

1. Which type of problem is house price prediction?
   Answer: Regression.
2. Does supervised learning require labeled data?
   Answer: Yes.
3. Name two supervised algorithms.
   Answer: Linear Regression and Decision Trees.

Summary

Supervised learning is one of the most widely used machine learning techniques for predictive modeling.

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