Explain in detail about the supervised learning approach by taking a suitable example
Answer:-
Supervised learning algorithms learn to map input data (x) to an output (y) using labeled examples in the training set.
5.7 Supervised Learning Algorithms with Examples:
1. Probabilistic Supervised Learning
- Definition: Estimates the probability distribution p(y | x), representing the likelihood of output y given input x.
- Example: Logistic Regression
- Use Case: Predicting whether an email is spam or not.
- Explanation: Logistic regression models p(y = 1 | x) = \sigma(\theta^T x), where \sigma(z) = \frac{1}{1 + e^{-z}} maps the input to probabilities. If the probability is greater than 0.5, the email is classified as spam; otherwise, it is classified as not spam.
2. Support Vector Machines (SVMs)
- Definition: Finds a hyperplane that separates classes with the maximum margin.
- Example: Image Classification
- Use Case: Classifying images of cats vs. dogs.
- Explanation: SVMs use w^T x + b to decide class labels. By applying the Gaussian (RBF) kernel k(u, v) = \exp\left(-\frac{|u - v|^2}{2\sigma^2}\right), the algorithm can classify data in non-linear cases where the decision boundary is not a straight line.
3. k-Nearest Neighbors (k-NN)
- Definition: A non-parametric algorithm that predicts output by identifying the k-nearest neighbors of a test point in the training data.
- Example: Movie Recommendation
- Use Case: Recommending movies based on user preferences.
- Explanation: If a user likes movies similar to their k nearest neighbors (other users with similar preferences), k-NN predicts the user will like a particular movie. For classification, each “neighbor” votes on the output.
4. Decision Trees
- Definition: Divides the input space into regions using decision rules at internal nodes.
- Example: Loan Approval Prediction
- Use Case: Determining whether a loan application should be approved based on features like income, credit score, and debt-to-income ratio.
- Explanation: The tree splits the dataset into regions based on rules like “Is income > $50,000?” or “Is credit score > 700?” Each region corresponds to a decision (e.g., approve or deny).
Summary with Examples:
- Probabilistic models (e.g., Logistic Regression): Predict email spam detection by estimating probabilities.
- SVMs: Classify images (e.g., cats vs. dogs) using a hyperplane, with kernel tricks for non-linear boundaries.
- k-NN: Recommend movies based on the preferences of the nearest neighbors.
- Decision Trees: Predict loan approvals by splitting data into decision regions.