INFO
Classification models are supervised learning algorithms that predict categorical labels by learning decision boundaries from labeled data. They are foundational in tasks like spam detection, medical diagnosis, and sentiment analysis.
Overview
Classification focuses on assigning input data to one or more predefined categories. Models learn patterns from labeled examples and generalize to unseen data. Tasks may be:
- Binary (e.g., spam vs. not spam)
- Multiclass (e.g., digit recognition)
- Multi-label (e.g., tagging multiple objects in an image)
Common applications include fraud detection, document classification, image recognition, and disease diagnosis.
Included Algorithms
Models the probability of class membership using a sigmoid function. Simple, interpretable, and effective for linearly separable data.
Splits data based on feature thresholds to form a tree of decisions. Easy to interpret but prone to overfitting.
Ensemble of decision trees trained on bootstrapped samples. Reduces overfitting and improves generalization.
Finds optimal hyperplanes to separate classes. Effective in high-dimensional spaces and with clear margins.
Probabilistic model based on Bayes’ theorem with strong independence assumptions. Fast and effective for text classification.
Classifies based on the majority label among nearest neighbors. Non-parametric and intuitive but computationally expensive at inference.
Builds models sequentially to correct errors of previous ones. Powerful and flexible but sensitive to hyperparameters.
Combines weak learners by focusing on misclassified instances. Works well with simple base models but can be sensitive to noise.
Optimizes linear classifiers using incremental updates. Scales well to large datasets but requires careful tuning.
Uses human-readable rules to assign labels. Transparent and interpretable, often used in expert systems.
Key Concepts
- Decision Boundaries: Separators between classes in feature space
- Loss Functions: Guide model optimization (e.g., cross-entropy)
- Hyperplanes: Used in SVM to separate classes
- Ensemble Learning: Combines multiple models for better performance
- Bias-Variance Tradeoff: Balancing underfitting and overfitting
Applications
- Email spam filtering
- Disease diagnosis from medical records
- Sentiment analysis in social media
- Image classification in computer vision
- Fraud detection in financial systems
Suggested Links
- Supervised Learning — Broader context for learning from labeled data
- Model Evaluation — Metrics like accuracy, precision, recall, F1-score, ROC-AUC
- Feature Engineering — Crucial for improving classification performance