Lilly Tech Systems
The 7 Most Used ML Algorithms
A roadmap to the machine learning algorithms that power the vast majority of real-world AI applications — from predicting house prices to detecting fraud.
Why These 7 Algorithms?
While there are hundreds of machine learning algorithms, a small set dominates real-world applications. These 7 algorithms cover the vast majority of problems you'll encounter in practice:
1. Linear Regression
The workhorse for predicting continuous values. House prices, sales forecasts, temperature predictions.
2. Logistic Regression
The go-to for binary and multi-class classification. Spam detection, disease diagnosis, customer churn.
3. Decision Trees
Interpretable, rule-based classification and regression. Credit scoring, medical diagnosis, business rules.
4. Random Forest
Ensemble of decision trees that reduces overfitting. Feature selection, anomaly detection, general-purpose ML.
5. Gradient Boosting
The king of tabular data and Kaggle competitions. XGBoost, LightGBM, CatBoost power production ML systems.
6. Neural Networks
The foundation of deep learning. Image recognition, NLP, speech synthesis, and complex pattern recognition.
7. Graph Neural Networks
Learning on graph-structured data. Social networks, molecular design, recommendation systems, fraud detection.
Supervised vs. Unsupervised Learning
Before diving into individual algorithms, it's important to understand the two main paradigms of machine learning:
| Aspect | Supervised Learning | Unsupervised Learning |
|---|---|---|
| Training Data | Labeled (input-output pairs) | Unlabeled (input only) |
| Goal | Predict outputs for new inputs | Find hidden patterns/structure |
| Examples | Classification, Regression | Clustering, Dimensionality Reduction |
| Algorithms (this course) | All 7 algorithms covered here | K-Means, PCA, DBSCAN (not covered) |
| Evaluation | Compare predictions to known answers | Silhouette score, reconstruction error |
Regression vs. Classification
Within supervised learning, there are two main task types:
- Regression: Predicting a continuous numeric value (e.g., price = $342,500). Algorithms: Linear Regression, Decision Trees, Random Forest, Gradient Boosting, Neural Networks.
- Classification: Predicting a discrete category (e.g., "spam" or "not spam"). Algorithms: Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, Neural Networks, GNNs.
Note that many algorithms (Decision Trees, Random Forest, Gradient Boosting, Neural Networks) can handle both regression and classification tasks.
Algorithm Selection Flowchart
Use this decision guide to choose the right algorithm for your problem:
- What type of output? Continuous number → Regression. Category → Classification.
- Need interpretability? Yes → Linear/Logistic Regression or Decision Trees. No → Continue.
- Tabular structured data? Yes → Gradient Boosting (XGBoost/LightGBM). This is the default choice for structured data.
- Image, text, or sequence data? Yes → Neural Networks (CNNs for images, Transformers for text, RNNs for sequences).
- Graph-structured data? Yes → Graph Neural Networks.
- Small dataset (<1000 samples)? Yes → Linear/Logistic Regression or Random Forest. Avoid neural networks.
- Need a strong baseline quickly? Random Forest is hard to beat without tuning.
Complexity vs. Interpretability Tradeoff
One of the most important considerations in choosing an algorithm is the tradeoff between model complexity (predictive power) and interpretability (ability to explain predictions):
| Algorithm | Interpretability | Complexity | Typical Accuracy | Best For |
|---|---|---|---|---|
| Linear Regression | ⭐⭐⭐⭐⭐ Very High | Low | Moderate | Simple relationships, baseline |
| Logistic Regression | ⭐⭐⭐⭐⭐ Very High | Low | Moderate | Binary classification baseline |
| Decision Trees | ⭐⭐⭐⭐ High | Medium | Moderate | Explainable decisions |
| Random Forest | ⭐⭐⭐ Medium | Medium-High | High | Robust general-purpose |
| Gradient Boosting | ⭐⭐ Low-Medium | High | Very High | Tabular data champion |
| Neural Networks | ⭐ Low | Very High | Highest (unstructured) | Images, text, sequences |
| Graph Neural Networks | ⭐ Low | Very High | Highest (graph data) | Graph-structured problems |
When to Use Each Algorithm
| Algorithm | Use When | Avoid When |
|---|---|---|
| Linear Regression | Linear relationships, need interpretable coefficients, quick baseline | Non-linear data, complex interactions, classification tasks |
| Logistic Regression | Binary/multi-class classification, need probabilities, interpretable model | Non-linear decision boundaries, complex feature interactions |
| Decision Trees | Need explainable rules, mixed feature types, quick prototyping | High-dimensional data, need generalization (single tree overfits) |
| Random Forest | General-purpose, robust baseline, feature importance, noisy data | Memory-constrained environments, need real-time predictions (slow) |
| Gradient Boosting | Tabular data, competitions, need maximum accuracy, structured data | Small datasets, need fast training, unstructured data (images/text) |
| Neural Networks | Images, text, audio, sequences, very large datasets, complex patterns | Small datasets, need interpretability, tabular data (usually) |
| GNNs | Social networks, molecules, knowledge graphs, relational data | Non-graph data, small graphs, need interpretability |
What's Next
In the following lessons, we'll dive deep into each algorithm one by one. For each algorithm, you'll learn:
- The mathematical foundation — understand exactly how it works
- The intuition — build mental models that help you reason about behavior
- Python code — production-ready implementations with real datasets
- When to use it — practical guidance for real-world applications
- Hyperparameter tuning — how to get the best performance
Let's start with the most fundamental algorithm: Linear Regression.