Compare algorithms side by side

Choosing between two algorithms is where most practical ML decisions happen. Each comparison below puts the contenders head to head — how they work, where they fail, what they cost to train — and links to an interactive visualization of each so you can see the difference instead of taking our word for it.

Random Forest vs Gradient BoostingRandom forest vs gradient boosting: how each builds trees, which is more accurate, which is easier to tune, and when to pick one over the other.
PCA vs t-SNE vs UMAPPCA vs t-SNE vs UMAP compared: linear projection vs neighbor embeddings, what each preserves and distorts, and which to use for visualization or features.
Ridge vs Lasso RegressionRidge vs lasso regression explained: L2 shrinks all coefficients while L1 zeros some out. Which regularization to choose, and when elastic net beats both.
Bagging vs BoostingBagging vs boosting: parallel averaging to cut variance vs sequential error-fixing to cut bias. How each works, when each fails, and which to choose.
K-Means vs DBSCANK-means vs DBSCAN: centroid clustering with a fixed k vs density clustering with noise detection. Strengths, failure modes, and how to choose.
Linear Regression vs Logistic RegressionLinear vs logistic regression: continuous prediction vs class probabilities. What actually differs, why the names mislead, and which to use when.