Scikit-Learn Cheat Sheet

Updated 2026-04-27

Next Topic: Self-Supervised and Contrastive Learning Cheat Sheet

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.

Table 1: Supervised Learning — Classification AlgorithmsTable 2: Supervised Learning — Regression AlgorithmsTable 3: Unsupervised Learning — Clustering AlgorithmsTable 4: Semi-Supervised LearningTable 5: Unsupervised Learning — Dimensionality ReductionTable 6: Clustering Evaluation MetricsTable 7: Data Preprocessing — Scaling and NormalizationTable 8: Data Preprocessing — Encoding Categorical VariablesTable 9: Data Preprocessing — Handling Missing ValuesTable 10: Feature Engineering and SelectionTable 11: Model Selection — Train/Test SplittingTable 12: Model Selection — Cross-ValidationTable 13: Hyperparameter TuningTable 14: Pipelines and CompositionTable 15: Classification MetricsTable 16: Regression MetricsTable 17: Text Feature ExtractionTable 18: Ensemble MethodsTable 19: Probability CalibrationTable 20: Multiclass and Multilabel StrategiesTable 21: Model InspectionTable 22: Advanced TechniquesTable 23: Model Persistence and Deployment

Table 1: Supervised Learning — Classification Algorithms

Algorithm	Example	Description
Logistic Regression	`from sklearn.linear_model import LogisticRegression` `clf = 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.
Random Forest Classifier	`from sklearn.ensemble import RandomForestClassifier` `clf = 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.
Gradient Boosting Classifier	`from sklearn.ensemble import GradientBoostingClassifier` `clf = 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.
Support Vector Classifier (SVC)	`from sklearn.svm import SVC` `clf = 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.
Decision Tree Classifier	`from sklearn.tree import DecisionTreeClassifier` `clf = 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.
K-Nearest Neighbors Classifier	`from sklearn.neighbors import KNeighborsClassifier` `clf = 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.
Naive Bayes — Gaussian	`from sklearn.naive_bayes import GaussianNB` `clf = 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.
Naive Bayes — Multinomial	`from sklearn.naive_bayes import MultinomialNB` `clf = 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.

Table 1: Supervised Learning — Classification Algorithms

Algorithm	Example	Description
Logistic Regression	`from sklearn.linear_model import LogisticRegression` `clf = 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.
Random Forest Classifier	`from sklearn.ensemble import RandomForestClassifier` `clf = 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.
Gradient Boosting Classifier	`from sklearn.ensemble import GradientBoostingClassifier` `clf = 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.
Support Vector Classifier (SVC)	`from sklearn.svm import SVC` `clf = 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.
Decision Tree Classifier	`from sklearn.tree import DecisionTreeClassifier` `clf = 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.
K-Nearest Neighbors Classifier	`from sklearn.neighbors import KNeighborsClassifier` `clf = 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.
Naive Bayes — Gaussian	`from sklearn.naive_bayes import GaussianNB` `clf = 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.
Naive Bayes — Multinomial	`from sklearn.naive_bayes import MultinomialNB` `clf = 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.