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Start with the task, target, and labels
Supervised learning fits a model to examples that include an input and a known outcome. A continuous outcome, such as revenue, calls for regression; a categorical outcome, such as churn or fraud, calls for classification. Unsupervised learning has no supplied target label and instead looks for structure, such as clusters or lower-dimensional representations.
Use the following list as a teaching selection, not a ranking or a requirement that every data scientist master every method.
The 12 algorithms
| Algorithm | Main job | What it does | Important trade-off |
|---|---|---|---|
| Linear regression | Continuous prediction | Estimates a linear relationship between features and a numeric target. The fitted coefficients are comparatively easy to inspect. | Useful as a baseline, but a straight-line form can miss nonlinear relationships and interactions. |
| Logistic regression | Classification | Uses a linear model to estimate class probabilities and make category decisions. | Fast and interpretable, but its decision boundary is limited unless features are transformed or expanded. |
| Naïve Bayes | Probabilistic classification | Combines Bayes’ rule with simplifying conditional-independence assumptions to estimate class probabilities. | Often a useful lightweight comparison, but the assumptions may be unsuitable when features strongly depend on one another. |
| k-nearest neighbors (k-NN) | Classification or regression | Predicts from the outcomes of the most similar labeled observations. | “Similar” depends on the distance measure and feature scales; prediction can become expensive with many observations or features. |
| Support vector machine (SVM) | Classification or regression | Finds a boundary with a large margin between classes; kernels can represent more complex boundaries. | Powerful on some feature spaces, but kernel, regularization, scaling, and data size choices require care. |
| Decision tree | Classification or regression | Applies a sequence of feature-based questions that can be visualized as rules. | Deep trees overfit and small data changes can produce different trees. Predictions are piecewise constant, so trees are poor extrapolators. |
| Random forest | Classification or regression | Averages or votes across many randomized decision trees. | Usually more stable than one tree, but less simple to explain and still needs out-of-sample validation. |
| Gradient boosting | Classification or regression | Builds an additive predictor in which successive learners focus on the remaining errors. | Can model complex patterns, but learning rate, number of learners, tree size, and stopping rules must be tuned to avoid overfitting. |
| k-means | Clustering | Partitions observations into a chosen number of groups around centroid locations. | You must choose the number of clusters, and scaling, distance geometry, initialization, and outliers affect the result. |
| Hierarchical clustering | Clustering | Builds nested groups that can be viewed as a hierarchy rather than returning only one partition. | Useful when relationships at several levels matter; the linkage and distance choices shape the hierarchy. |
| Principal component analysis (PCA) | Dimensionality reduction | Rotates correlated features into fewer uncorrelated components that retain directions of variance. | Components can simplify modeling and visualization, but they are combinations of original features and may be harder to explain. |
| Neural network | Flexible prediction and representation | Layered functions can represent nonlinear interactions; neural networks have both supervised and unsupervised forms. | They generally demand thoughtful architecture, regularization, optimization, and data preparation, so they are not the automatic first choice. |
How to choose between them
1. Match the output to the problem
- For a numeric target, begin with linear regression, then compare a tree, random forest, gradient boosting, SVM, k-NN, or neural network when the relationship may be nonlinear.
- For categories, use logistic regression, naïve Bayes, k-NN, SVM, a decision tree, a forest, boosting, or a neural network.
- With no target labels, consider k-means or hierarchical clustering for groups and PCA for a compact representation.
2. Check the feature geometry and preparation
Scaling matters particularly for distance- and margin-based methods such as k-NN and SVM. Encoding categorical variables, handling missing values, and deciding whether to transform skewed features can change the comparison. Tree ensembles are generally less sensitive to feature scaling, but they are not exempt from missing-data and leakage problems. PCA should usually be applied after features are put on a meaningful common scale when units differ.
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3. Weigh explanation against flexibility
Coefficients from linear or logistic regression and the rules of a small decision tree are relatively direct to communicate. Forests, boosting models, and neural networks can capture richer interactions but require additional explanation tools and governance. A model that stakeholders can audit may be preferable to a marginally stronger but opaque alternative.
4. Account for computation and maintenance
Training and prediction costs vary with the number of rows, features, neighbors, trees, boosting iterations, and network parameters. k-NN shifts much of the work to prediction; ensembles and neural networks add tuning and monitoring decisions. Include retraining time, latency, memory, and operational complexity—not just fitting time—when selecting a production method.
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- Use scikit-learn to track an example ML project end to end
- Explore several models, including support vector machines, decision trees, random forests, and ensemble methods
- Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection
- Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers
- Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning
Evaluate generalization, not memorization
A model can fit its training data while failing on new cases. Keep a final test set separate from model selection whenever the data volume and collection process allow it. On the remaining training data, use cross-validation to compare candidates and tune hyperparameters. Select metrics that reflect the decision: a generic accuracy score is not appropriate for every classification or regression problem.
- Use the same preprocessing logic inside each validation split to prevent information leakage.
- Inspect errors by important segments, time periods, and classes, not only an overall score.
- For clustering and PCA, evaluate stability and usefulness to the domain; there is no target label whose predictive score settles the question.
- Record the data version, features, preprocessing, algorithm settings, and evaluation split so that results can be reproduced.
A practical learning sequence
- Define the target, prediction horizon, and cost of mistakes.
- Build a simple baseline: linear regression for a continuous target or logistic regression for a categorical one.
- Add one nonlinear single model, such as a constrained decision tree, and one ensemble, such as a random forest or gradient boosting.
- Compare a distance or margin method—k-NN or SVM—only after checking scaling and feature geometry.
- If labels are unavailable, test k-means and hierarchical clustering for grouping questions, and PCA when fewer dimensions are needed.
- Try a neural network when the data, representation, and operational budget justify its extra complexity.
- Choose using held-out performance, validation stability, interpretability, resource requirements, and the consequences of errors.
What “every data scientist should know” really means
Knowing an algorithm means understanding the question it answers, its assumptions, the preparation it needs, how it can overfit, and how to evaluate it—not memorizing an API call. As CFA Institute’s 2026 Machine Learning reading puts the intuition: “An elementary way to think of ML algorithms is to ‘find the pattern, apply the pattern.’” The responsible practitioner also checks whether the pattern survives on data the model did not see and whether it is useful for the people and process it serves.
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