Siksha Sarovar

Siksha Sarovar (sikshasarovar.com) is a free educational web application that helps students in India learn programming and prepare for academic and competitive exams. The platform offers structured coding courses (C, C++, Python, Java, HTML, CSS, PHP, Power BI, AI, Machine Learning, Data Science), complete university curriculum notes for BCA/MCA students with previous year question papers, Class 10 and Class 12 CBSE/HBSE school notes, and dedicated preparation material for SSC, UPSC, Banking, Railway and other government exams. Browsing the site is completely free and requires no account. Users may optionally sign in with Google solely to save their learning progress, quiz scores and personal preferences across devices.

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Siksha Sarovar is a free e-learning platform for coding courses, BCA university notes and competitive exam preparation. Optional Google sign-in saves your learning progress across devices.

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8. Supervised Learning – Classification

Lesson 8 of 21 in the free Machine Learning notes on Siksha Sarovar, written by Rohit Jangra.

What is Classification?

Classification is the task of predicting a discrete class label for a given input. Unlike regression, which predicts a number, classification predicts a category.

Goal: Assign a label (e.g., Spam/Ham, Benign/Malignant, Cat/Dog).

Popular Algorithms

  1. Logistic Regression: confuse data? No! Despite the name, it's for classification. It predicts probabilities (0 to 1) using a Sigmoid function.
  2. K-Nearest Neighbors (KNN): "Tell me who your friends are, and I'll tell you who you are." Classifies based on the majority class of nearest neighbors.
  3. Support Vector Machines (SVM): Finds the best hyperplane that separates classes with the maximum margin.
  4. Decision Trees: Flowchart-like structures making decisions based on features (e.g., "If Age > 30...").

Evaluation Metrics

MetricDefinition
AccuracyCorrect Predictions / Total Predictions. (Can be misleading in imbalanced datasets).
Precision"Of all predicted positives, how many were actually positive?" (Low False Positives).
Recall"Of all actual positives, how many did we find?" (Low False Negatives).
F1-ScoreHarmonic mean of Precision and Recall. Good balance.