Traditional Machine Learning
Overview
Traditional machine learning encompasses algorithms that were developed before the deep learning revolution. These methods remain highly relevant and often outperform neural networks on structured data, small datasets, and when interpretability is crucial.
Traditional ML algorithms are generally divided into two main categories: supervised learning (learning from labeled examples) and unsupervised learning (finding patterns in unlabeled data).
Supervised Learning
Supervised learning algorithms learn from input-output pairs to make predictions on new data.
Classification Algorithms
- SVM:Creates optimal decision boundaries with maximum margin
- KNN:Classifies based on majority vote of nearest neighbors
- Decision Trees:Creates interpretable tree-like decision rules
- Random Forest:Ensemble of decision trees for better generalization
- Naive Bayes:Probabilistic classifier based on Bayes' theorem
Key Characteristics
- • Interpretable: Most traditional ML models are explainable
- • Fast Training: Generally require less computational resources
- • Small Data Friendly: Work well with limited training data
- • Feature Engineering: Performance often depends on good features
- • Domain Knowledge: Benefit from incorporating expert knowledge
Unsupervised Learning
Unsupervised learning finds hidden patterns and structures in data without labeled examples.
Clustering Algorithms
- K-Means:Partitions data into k spherical clusters
- Hierarchical:Creates tree-like cluster hierarchies
- DBSCAN:Density-based clustering that finds arbitrary shapes
Dimensionality Reduction
- PCA:Finds principal components that capture maximum variance
- LDA:Linear discriminant analysis for supervised dim reduction
- t-SNE:Non-linear method for visualization
When to Use Traditional ML
✓ Good Fit When:
- • Working with structured/tabular data
- • Small to medium-sized datasets (<100k samples)
- • Interpretability is critical
- • Limited computational resources
- • Need fast training and inference
- • Domain expertise can guide feature engineering
- • Regulatory requirements for explainability
✗ Consider Alternatives When:
- • Working with unstructured data (images, text, audio)
- • Very large datasets (millions+ samples)
- • Complex non-linear relationships
- • High-dimensional raw data
- • Automatic feature learning is preferred
- • State-of-the-art performance is critical
- • End-to-end learning is beneficial
Algorithm Selection Guide
| Algorithm | Type | Best For | Pros | Cons |
|---|---|---|---|---|
| SVM | Classification/Regression | High-dimensional data, small datasets | Strong theoretical foundation, kernel trick | Slow on large datasets, parameter tuning |
| KNN | Classification/Regression | Non-linear patterns, local decisions | Simple, no assumptions about data | Slow inference, curse of dimensionality |
| Decision Trees | Classification/Regression | Interpretable models, mixed data types | Highly interpretable, handles missing values | Prone to overfitting, unstable |
| Random Forest | Classification/Regression | General purpose, tabular data | Robust, feature importance, less overfitting | Less interpretable than single tree |
| K-Means | Clustering | Spherical clusters, known number of clusters | Fast, simple, scalable | Assumes spherical clusters, need to specify k |
| PCA | Dimensionality Reduction | Data visualization, noise reduction | Removes redundancy, interpretable components | Linear assumptions, loses information |
Next Steps
In the following lessons, we'll dive deep into each algorithm with interactive visualizations that let you experiment with different parameters and see how they affect the algorithm's behavior.
💡 Learning Tip: Start with the algorithms most relevant to your domain. If you're working with tabular data, begin with Random Forest and SVM. For exploratory data analysis, start with K-Means and PCA.