Scikit-learn (sklearn) is Python's most widely adopted machine learning library, built on NumPy, SciPy, and Matplotlib to provide simple, efficient tools for predictive data analysis. It offers a unified, consistent API across hundreds of algorithms — from linear regression to Gaussian processes — along with essential preprocessing, model selection, and evaluation utilities. Scikit-learn's ease of use and production-ready implementations make it the go-to library for both rapid prototyping and deploying ML models at scale, covering supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), semi-supervised learning, and the full pipeline of data preparation to model deployment.
What This Cheat Sheet Covers
This topic spans 23 focused tables and 162 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.
A jump-to index of every table row in this cheat sheet.
An interactive map of every table and concept in this topic.
Table 1: Supervised Learning — Classification Algorithms
Classification is the most common starting point in scikit-learn — every estimator here predicts a discrete label and shares the same fit/predict rhythm. The list spans the full spectrum you'll actually reach for: fast linear baselines like Logistic Regression and Perceptron, the tree ensembles (Random Forest, Gradient Boosting, AdaBoost) that win most tabular problems, kernel SVMs for tricky boundaries, and the Naive Bayes family for text. A useful mental shortcut is to start simple, then trade interpretability for accuracy as you move down the table.
| Algorithm | Example | Description |
|---|---|---|
from sklearn.linear_model import LogisticRegressionclf = LogisticRegression()clf.fit(X_train, y_train) | • Binary or multiclass linear classifier using logistic function to model probability • supports L1, L2, or ElasticNet regularization to prevent overfitting. | |
from sklearn.ensemble import RandomForestClassifierclf = RandomForestClassifier(n_estimators=100)clf.fit(X_train, y_train) | • Ensemble of decision trees trained on bootstrap samples with random feature subsets • averages predictions to reduce variance and provides feature importance scores. | |
from sklearn.ensemble import GradientBoostingClassifierclf = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1)clf.fit(X_train, y_train) | • Sequentially builds trees where each corrects errors of previous ones • learning rate controls contribution of each tree; powerful but sensitive to overfitting. | |
from sklearn.svm import SVCclf = SVC(kernel='rbf', C=1.0)clf.fit(X_train, y_train) | • Finds optimal hyperplane separating classes • uses kernel trick (linear, RBF, polynomial, sigmoid) for non-linear boundaries • C parameter controls margin vs. misclassification trade-off. | |
from sklearn.tree import DecisionTreeClassifierclf = DecisionTreeClassifier(max_depth=5)clf.fit(X_train, y_train) | • Recursively splits data based on feature thresholds to minimize impurity (Gini or entropy) • interpretable but prone to overfitting without depth limits. | |
from sklearn.neighbors import KNeighborsClassifierclf = KNeighborsClassifier(n_neighbors=5)clf.fit(X_train, y_train) | • Non-parametric lazy learner assigning class by majority vote of k nearest neighbors • distance-based — requires feature scaling for optimal results. | |
from sklearn.naive_bayes import GaussianNBclf = GaussianNB()clf.fit(X_train, y_train) | • Assumes features follow Gaussian distribution; applies Bayes' theorem with naive independence assumption • fast and effective for continuous features. | |
from sklearn.naive_bayes import MultinomialNBclf = MultinomialNB(alpha=1.0)clf.fit(X_train, y_train) | • Designed for discrete count data (e.g., word counts) • alpha adds Laplace smoothing; commonly used for document classification. |