13 May What is Classification in Machine Learning

Classification is a fundamental type of supervised learning where the goal is to predict discrete categories or classes. Let us understand the concept.

Core Concepts of Classification

1. What is Classification?
   • Predicting categorical labels (classes) rather than continuous values
   • Examples: spam/not spam, disease diagnosis, image recognition
2. Key Terminology
   • Features/Input Variables: The characteristics used to make predictions
   • Target Variable: The class label you’re trying to predict
   • Decision Boundary: The surface that separates different classes

Common Classification Algorithms

1. Logistic Regression
   • Despite its name, it’s for classification
   • Outputs probabilities between 0 and 1
   • Uses a sigmoid function
2. Decision Trees
   • Tree-like model of decisions
   • Simple to understand and visualize
3. Random Forest
   • Ensemble of many decision trees
   • Reduces overfitting compared to single trees
4. Support Vector Machines (SVM)
   • Finds the optimal separating hyperplane
   • Works well with a clear margin of separation
5. k-Nearest Neighbors (k-NN)
   • Classifies based on majority vote of nearest neighbors
   • Simple but computationally intensive

Evaluation Metrics

Unlike regression, we can’t use metrics like MSE. Instead, we use:

• Accuracy: (Correct predictions)/(Total predictions)
• Precision: True positives/(True positives + False positives)
• Recall: True positives/(True positives + False negatives)
• F1 Score: Harmonic mean of precision and recall
• Confusion Matrix: Table showing correct and incorrect predictions

Common Challenges

1. Imbalanced Classes
   • When one class dominates the dataset
   • Solutions: Resampling, different evaluation metrics, class weights
2. Overfitting
   • Model performs well on training data but poorly on new data
   • Solutions: Regularization, cross-validation, simpler models
3. Feature Importance
   • Not all features contribute equally
   • Can use feature selection techniques

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