scikit-learn Cheatsheet

Installation & Imports

Installation: pip install scikit-learn

Install scikit-learn and common dependencies.

# Install scikit-learn
pip install scikit-learn
# Install with additional packages
pip install scikit-learn pandas numpy matplotlib
# Upgrade to latest version
pip install scikit-learn --upgrade

Essential Imports

Standard imports for scikit-learn workflows.

# Core imports
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score,
classification_report
# Common algorithms
from sklearn.linear_model import LinearRegression,
LogisticRegression
from sklearn.ensemble import RandomForestClassifier,
GradientBoostingRegressor
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier

Check Version

Verify your scikit-learn installation.

import sklearn
print(sklearn.__version__)
# Show build configuration
sklearn.show_versions()

Dataset Loading

Load built-in datasets for practice.

from sklearn.datasets import load_iris, load_boston,
make_classification
# Load sample datasets
iris = load_iris()
X, y = iris.data, iris.target
# Generate synthetic data
X_synth, y_synth = make_classification(n_samples=1000,
n_features=20, n_informative=10, random_state=42)

Data Preprocessing

Train-Test Split: train_test_split()

Divide data into training and testing sets.

# Basic split (80% train, 20% test)
X_train, X_test, y_train, y_test =
train_test_split(X, y, test_size=0.2,
random_state=42)
# Stratified split for classification
X_train, X_test, y_train, y_test =
train_test_split(X, y, test_size=0.2,
stratify=y, random_state=42)
# Multiple splits
X_train, X_temp, y_train, y_temp =
train_test_split(X, y, test_size=0.4,
random_state=42)
X_val, X_test, y_val, y_test =
train_test_split(X_temp, y_temp,
test_size=0.5, random_state=42)

Feature Scaling: StandardScaler() / MinMaxScaler()

Normalize features to similar scales.

# Standardization (mean=0, std=1)
from sklearn.preprocessing import
StandardScaler, MinMaxScaler
scaler = StandardScaler()
X_scaled =
scaler.fit_transform(X_train)
X_test_scaled =
scaler.transform(X_test)
# Min-Max scaling (0-1 range)
minmax_scaler = MinMaxScaler()
X_minmax =
minmax_scaler.fit_transform(X_train)
X_test_minmax =
minmax_scaler.transform(X_test)

Encoding: LabelEncoder() / OneHotEncoder()

Convert categorical variables to numerical format.

# Label encoding for target variable
from sklearn.preprocessing import
LabelEncoder, OneHotEncoder
label_encoder = LabelEncoder()
y_encoded =
label_encoder.fit_transform(y)
# One-hot encoding for categorical
features
from sklearn.preprocessing import
OneHotEncoder
encoder =
OneHotEncoder(sparse=False,
drop='first')
X_encoded =
encoder.fit_transform(X_categorical)
# Get feature names
feature_names =
encoder.get_feature_names_out()

Supervised Learning - Classification

Logistic Regression: LogisticRegression()

Linear model for binary and multiclass classification.

# Basic logistic regression
from sklearn.linear_model import LogisticRegression
log_reg = LogisticRegression(random_state=42)
log_reg.fit(X_train, y_train)
y_pred = log_reg.predict(X_test)
y_proba = log_reg.predict_proba(X_test)
# With regularization
log_reg_l2 = LogisticRegression(C=0.1, penalty='l2')
log_reg_l1 = LogisticRegression(C=0.1, penalty='l1',
solver='liblinear')

Decision Tree: DecisionTreeClassifier()

Tree-based model for classification tasks.

# Decision tree classifier
from sklearn.tree import DecisionTreeClassifier
tree_clf = DecisionTreeClassifier(max_depth=5,
random_state=42)
tree_clf.fit(X_train, y_train)
y_pred = tree_clf.predict(X_test)
# Feature importance
importances = tree_clf.feature_importances_
# Visualize tree
from sklearn.tree import plot_tree
plot_tree(tree_clf, max_depth=3, filled=True)

Random Forest: RandomForestClassifier()

Ensemble method combining multiple decision trees.

# Random forest classifier
from sklearn.ensemble import RandomForestClassifier
rf_clf = RandomForestClassifier(n_estimators=100,
random_state=42)
rf_clf.fit(X_train, y_train)
y_pred = rf_clf.predict(X_test)
# Hyperparameter tuning
rf_clf = RandomForestClassifier(
    n_estimators=200,
    max_depth=10,
    min_samples_split=5,
    min_samples_leaf=2,
    random_state=42
)

Support Vector Machine: SVC()

Powerful classifier using kernel methods.

# SVM classifier
from sklearn.svm import SVC
svm_clf = SVC(kernel='rbf', C=1.0, gamma='scale',
random_state=42)
svm_clf.fit(X_train, y_train)
y_pred = svm_clf.predict(X_test)
# Different kernels
svm_linear = SVC(kernel='linear')
svm_poly = SVC(kernel='poly', degree=3)
svm_rbf = SVC(kernel='rbf', gamma=0.1)

Supervised Learning - Regression

Linear Regression: LinearRegression()

Basic linear model for continuous target variables.

# Simple linear regression
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(X_train, y_train)
y_pred = lin_reg.predict(X_test)
# Get coefficients and intercept
coefficients = lin_reg.coef_
intercept = lin_reg.intercept_
print(f"R² score: {lin_reg.score(X_test, y_test)}")

Ridge Regression: Ridge()

Linear regression with L2 regularization.

# Ridge regression (L2 regularization)
from sklearn.linear_model import Ridge
ridge_reg = Ridge(alpha=1.0)
ridge_reg.fit(X_train, y_train)
y_pred = ridge_reg.predict(X_test)
# Cross-validation for alpha selection
from sklearn.linear_model import RidgeCV
ridge_cv = RidgeCV(alphas=[0.1, 1.0, 10.0])
ridge_cv.fit(X_train, y_train)

Lasso Regression: Lasso()

Linear regression with L1 regularization for feature selection.

# Lasso regression (L1 regularization)
from sklearn.linear_model import Lasso
lasso_reg = Lasso(alpha=0.1)
lasso_reg.fit(X_train, y_train)
y_pred = lasso_reg.predict(X_test)
# Feature selection (non-zero coefficients)
selected_features = X.columns[lasso_reg.coef_ != 0]
print(f"Selected features: {len(selected_features)}")

Random Forest Regression: RandomForestRegressor()

Ensemble method for regression tasks.

# Random forest regressor
from sklearn.ensemble import RandomForestRegressor
rf_reg = RandomForestRegressor(n_estimators=100,
random_state=42)
rf_reg.fit(X_train, y_train)
y_pred = rf_reg.predict(X_test)
# Feature importance for regression
feature_importance = rf_reg.feature_importances_

Model Evaluation

Classification Metrics

Evaluate classification model performance.

# Basic accuracy
from sklearn.metrics import accuracy_score,
precision_score, recall_score, f1_score
accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred,
average='weighted')
recall = recall_score(y_test, y_pred, average='weighted')
f1 = f1_score(y_test, y_pred, average='weighted')
# Detailed classification report
from sklearn.metrics import classification_report
print(classification_report(y_test, y_pred))
# Confusion matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)

ROC Curve & AUC

Plot ROC curve and calculate Area Under Curve.

# ROC curve for binary classification
from sklearn.metrics import roc_curve, auc
fpr, tpr, thresholds = roc_curve(y_test, y_proba[:, 1])
roc_auc = auc(fpr, tpr)
# Plot ROC curve
import matplotlib.pyplot as plt
plt.figure(figsize=(8, 6))
plt.plot(fpr, tpr, label=f'ROC Curve (AUC = {roc_auc:.2f})')
plt.plot([0, 1], [0, 1], 'k--')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.legend()

Regression Metrics

Evaluate regression model performance.

# Regression metrics
from sklearn.metrics import mean_squared_error,
mean_absolute_error, r2_score
mse = mean_squared_error(y_test, y_pred)
rmse = np.sqrt(mse)
mae = mean_absolute_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
print(f"MSE: {mse:.4f}")
print(f"RMSE: {rmse:.4f}")
print(f"MAE: {mae:.4f}")
print(f"R²: {r2:.4f}")

Cross-Validation

Robust model evaluation using cross-validation.

# K-fold cross-validation
from sklearn.model_selection import cross_val_score,
StratifiedKFold
cv_scores = cross_val_score(model, X, y, cv=5,
scoring='accuracy')
print(f"CV Accuracy: {cv_scores.mean():.4f} (+/-
{cv_scores.std() * 2:.4f})")
# Stratified K-fold for imbalanced datasets
skf = StratifiedKFold(n_splits=5, shuffle=True,
random_state=42)
cv_scores = cross_val_score(model, X, y, cv=skf,
scoring='f1_weighted')

Unsupervised Learning

K-Means Clustering: KMeans()

Partition data into k clusters.

# K-means clustering
from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=3, random_state=42)
cluster_labels = kmeans.fit_predict(X)
centroids = kmeans.cluster_centers_
# Determine optimal number of clusters (Elbow method)
inertias = []
K_range = range(1, 11)
for k in K_range:
    kmeans = KMeans(n_clusters=k, random_state=42)
    kmeans.fit(X)
    inertias.append(kmeans.inertia_)

Principal Component Analysis: PCA()

Dimensionality reduction technique.

# PCA for dimensionality reduction
from sklearn.decomposition import PCA
pca = PCA(n_components=2)
X_pca = pca.fit_transform(X)
explained_variance = pca.explained_variance_ratio_
# Find optimal number of components
pca_full = PCA()
pca_full.fit(X)
cumsum =
np.cumsum(pca_full.explained_variance_ratio_)
# Find components for 95% variance
n_components = np.argmax(cumsum >= 0.95) + 1

DBSCAN Clustering: DBSCAN()

Density-based clustering algorithm.

# DBSCAN clustering
from sklearn.cluster import DBSCAN
dbscan = DBSCAN(eps=0.5, min_samples=5)
cluster_labels = dbscan.fit_predict(X)
n_clusters = len(set(cluster_labels)) - (1 if -1 in
cluster_labels else 0)
n_noise = list(cluster_labels).count(-1)
print(f"Number of clusters: {n_clusters}")
print(f"Number of noise points: {n_noise}")

Hierarchical Clustering: AgglomerativeClustering()

Build hierarchy of clusters.

# Agglomerative clustering
from sklearn.cluster import AgglomerativeClustering
agg_clustering = AgglomerativeClustering(n_clusters=3,
linkage='ward')
cluster_labels = agg_clustering.fit_predict(X)
# Dendrogram visualization
from scipy.cluster.hierarchy import dendrogram, linkage
linked = linkage(X, 'ward')
plt.figure(figsize=(12, 8))
dendrogram(linked)

Model Selection & Hyperparameter Tuning

Grid Search: GridSearchCV()

Exhaustive search over parameter grid.

# Grid search for hyperparameter tuning
from sklearn.model_selection import GridSearchCV
param_grid = {
    'n_estimators': [100, 200, 300],
    'max_depth': [3, 5, 7, None],
    'min_samples_split': [2, 5, 10]
}
grid_search = GridSearchCV(
    RandomForestClassifier(random_state=42),
    param_grid, cv=5, scoring='accuracy', n_jobs=-1
)
grid_search.fit(X_train, y_train)
best_model = grid_search.best_estimator_
best_params = grid_search.best_params_

Random Search: RandomizedSearchCV()

Random sampling from parameter distributions.

# Randomized search (faster for large parameter spaces)
from sklearn.model_selection import
RandomizedSearchCV
from scipy.stats import randint
param_dist = {
    'n_estimators': randint(100, 500),
    'max_depth': [3, 5, 7, None],
    'min_samples_split': randint(2, 11)
}
random_search = RandomizedSearchCV(
    RandomForestClassifier(random_state=42),
    param_dist, n_iter=50, cv=5, scoring='accuracy',
n_jobs=-1, random_state=42
)
random_search.fit(X_train, y_train)

Pipeline: Pipeline()

Chain preprocessing and modeling steps.

# Create preprocessing and modeling pipeline
from sklearn.pipeline import Pipeline
pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('classifier', RandomForestClassifier(random_state=42))
])
pipeline.fit(X_train, y_train)
y_pred = pipeline.predict(X_test)
# Pipeline with grid search
param_grid = {
    'classifier__n_estimators': [100, 200],
    'classifier__max_depth': [3, 5, None]
}
grid_search = GridSearchCV(pipeline, param_grid, cv=5)
grid_search.fit(X_train, y_train)

Feature Selection: SelectKBest() / RFE()

Select the most informative features.

# Univariate feature selection
from sklearn.feature_selection import SelectKBest,
f_classif
selector = SelectKBest(score_func=f_classif, k=10)
X_selected = selector.fit_transform(X_train, y_train)
selected_features = X.columns[selector.get_support()]
# Recursive Feature Elimination
from sklearn.feature_selection import RFE
rfe = RFE(RandomForestClassifier(random_state=42),
n_features_to_select=10)
X_rfe = rfe.fit_transform(X_train, y_train)

Advanced Techniques

Ensemble Methods: VotingClassifier() / BaggingClassifier()

Combine multiple models for better performance.

# Voting classifier (ensemble of different algorithms)
from sklearn.ensemble import VotingClassifier
voting_clf = VotingClassifier(
    estimators=[
        ('lr', LogisticRegression(random_state=42)),
        ('rf', RandomForestClassifier(random_state=42)),
        ('svm', SVC(probability=True, random_state=42))
    ], voting='soft'
)
voting_clf.fit(X_train, y_train)
y_pred = voting_clf.predict(X_test)
# Bagging classifier
from sklearn.ensemble import BaggingClassifier
bagging_clf = BaggingClassifier(DecisionTreeClassifier(),
n_estimators=100, random_state=42)
bagging_clf.fit(X_train, y_train)

Gradient Boosting: GradientBoostingClassifier()

Sequential ensemble method with error correction.

# Gradient boosting classifier
from sklearn.ensemble import
GradientBoostingClassifier
gb_clf = GradientBoostingClassifier(n_estimators=100,
learning_rate=0.1, random_state=42)
gb_clf.fit(X_train, y_train)
y_pred = gb_clf.predict(X_test)
# Feature importance
importances = gb_clf.feature_importances_
# Learning curve
from sklearn.model_selection import learning_curve
train_sizes, train_scores, val_scores =
learning_curve(gb_clf, X, y, cv=5)

Handling Imbalanced Data: SMOTE() / Class Weights

Address class imbalance in datasets.

# Install imbalanced-learn: pip install imbalanced-learn
from imblearn.over_sampling import SMOTE
smote = SMOTE(random_state=42)
X_resampled, y_resampled = smote.fit_resample(X_train,
y_train)
# Using class weights
rf_balanced =
RandomForestClassifier(class_weight='balanced',
random_state=42)
rf_balanced.fit(X_train, y_train)
# Manual class weights
from sklearn.utils.class_weight import
compute_class_weight
class_weights = compute_class_weight('balanced',
classes=np.unique(y_train), y=y_train)
weight_dict = dict(zip(np.unique(y_train), class_weights))

Model Persistence: joblib

Save and load trained models.

# Save model
import joblib
joblib.dump(model, 'trained_model.pkl')
# Load model
loaded_model = joblib.load('trained_model.pkl')
y_pred = loaded_model.predict(X_test)
# Save entire pipeline
joblib.dump(pipeline, 'preprocessing_pipeline.pkl')
loaded_pipeline =
joblib.load('preprocessing_pipeline.pkl')
# Alternative using pickle
import pickle
with open('model.pkl', 'wb') as f:
    pickle.dump(model, f)
with open('model.pkl', 'rb') as f:
    loaded_model = pickle.load(f)

Performance & Debugging

Learning Curves: learning_curve()

Diagnose overfitting and underfitting.

# Plot learning curves
from sklearn.model_selection import learning_curve
train_sizes, train_scores, val_scores = learning_curve(
    model, X, y, cv=5, train_sizes=np.linspace(0.1, 1.0, 10)
)
plt.figure(figsize=(10, 6))
plt.plot(train_sizes, np.mean(train_scores, axis=1), 'o-',
label='Training Score')
plt.plot(train_sizes, np.mean(val_scores, axis=1), 'o-',
label='Validation Score')
plt.xlabel('Training Set Size')
plt.ylabel('Score')
plt.legend()

Validation Curves: validation_curve()

Analyze the effect of hyperparameters.

# Validation curve for single hyperparameter
from sklearn.model_selection import validation_curve
param_range = [10, 50, 100, 200, 500]
train_scores, val_scores = validation_curve(
    RandomForestClassifier(random_state=42), X, y,
    param_name='n_estimators',
param_range=param_range, cv=5
)
plt.figure(figsize=(10, 6))
plt.plot(param_range, np.mean(train_scores, axis=1), 'o-',
label='Training')
plt.plot(param_range, np.mean(val_scores, axis=1), 'o-',
label='Validation')
plt.xlabel('Number of Estimators')
plt.ylabel('Score')

Feature Importance Visualization

Understand which features drive model predictions.

# Plot feature importance
importances = model.feature_importances_
indices = np.argsort(importances)[::-1]
plt.figure(figsize=(12, 8))
plt.title("Feature Importance")
plt.bar(range(X.shape[1]), importances[indices])
plt.xticks(range(X.shape[1]), [X.columns[i] for i in indices],
rotation=90)
# SHAP values for model interpretability
# pip install shap
import shap
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X_test)
shap.summary_plot(shap_values, X_test)

Model Comparison

Compare multiple algorithms systematically.

# Compare multiple models
from sklearn.model_selection import cross_val_score
models = {
    'Logistic Regression':
LogisticRegression(random_state=42),
    'Random Forest':
RandomForestClassifier(random_state=42),
    'SVM': SVC(random_state=42),
    'Gradient Boosting':
GradientBoostingClassifier(random_state=42)
}
results = {}
for name, model in models.items():
    scores = cross_val_score(model, X_train, y_train, cv=5,
scoring='accuracy')
    results[name] = scores.mean()
    print(f"{name}: {scores.mean():.4f} (+/- {scores.std() *
2:.4f})")

Configuration & Best Practices

Random State & Reproducibility

Ensure consistent results across runs.

# Set random state for
reproducibility
import numpy as np
np.random.seed(42)
# Set random_state in all
sklearn components
model =
RandomForestClassifier(random
_state=42)
train_test_split(X, y,
random_state=42)
# For cross-validation
cv = StratifiedKFold(n_splits=5,
shuffle=True, random_state=42)

Memory & Performance

Optimize for large datasets and computational efficiency.

# Use n_jobs=-1 for parallel
processing
model =
RandomForestClassifier(n_jobs=
-1)
grid_search =
GridSearchCV(model,
param_grid, n_jobs=-1)
# For large datasets, use
partial_fit when available
from sklearn.linear_model
import SGDClassifier
sgd = SGDClassifier()
# Process data in chunks
for chunk in chunks:
    sgd.partial_fit(chunk_X,
chunk_y)

Warnings & Debugging

Handle common issues and debug models.

# Suppress warnings (use
carefully)
import warnings
warnings.filterwarnings('ignore')
# Enable sklearn's set_config for
better debugging
from sklearn import set_config
set_config(display='diagram')  #
Enhanced display in Jupyter
# Check for data leakage
from sklearn.model_selection
import cross_val_score
# Ensure preprocessing is done
inside CV loop

Relevant Links

• Python Cheatsheet
• Pandas Cheatsheet
• NumPy Cheatsheet
• Matplotlib Cheatsheet
• Data Science Cheatsheet
• Database Cheatsheet