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Zhongwei Li
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skills/scikit-learn/references/quick_reference.md
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skills/scikit-learn/references/quick_reference.md
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# Scikit-learn Quick Reference
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## Common Import Patterns
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```python
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# Core scikit-learn
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import sklearn
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# Data splitting and cross-validation
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from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV
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# Preprocessing
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from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder
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from sklearn.impute import SimpleImputer
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# Feature selection
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from sklearn.feature_selection import SelectKBest, RFE
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# Supervised learning
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from sklearn.linear_model import LogisticRegression, Ridge, Lasso
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from sklearn.ensemble import RandomForestClassifier, GradientBoostingRegressor
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from sklearn.svm import SVC, SVR
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from sklearn.tree import DecisionTreeClassifier
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# Unsupervised learning
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from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering
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from sklearn.decomposition import PCA, NMF
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# Metrics
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from sklearn.metrics import (
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accuracy_score, precision_score, recall_score, f1_score,
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mean_squared_error, r2_score, confusion_matrix, classification_report
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)
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# Pipeline
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from sklearn.pipeline import Pipeline, make_pipeline
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from sklearn.compose import ColumnTransformer, make_column_transformer
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# Utilities
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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```
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## Installation
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```bash
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# Using uv (recommended)
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uv pip install scikit-learn
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# Optional dependencies
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uv pip install scikit-learn[plots] # For plotting utilities
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uv pip install pandas numpy matplotlib seaborn # Common companions
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```
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## Quick Workflow Templates
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### Classification Pipeline
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```python
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import StandardScaler
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.metrics import classification_report, confusion_matrix
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# Split data
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, test_size=0.2, stratify=y, random_state=42
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)
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# Preprocess
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scaler = StandardScaler()
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X_train_scaled = scaler.fit_transform(X_train)
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X_test_scaled = scaler.transform(X_test)
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# Train
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model = RandomForestClassifier(n_estimators=100, random_state=42)
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model.fit(X_train_scaled, y_train)
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# Evaluate
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y_pred = model.predict(X_test_scaled)
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print(classification_report(y_test, y_pred))
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print(confusion_matrix(y_test, y_pred))
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```
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### Regression Pipeline
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```python
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import StandardScaler
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from sklearn.ensemble import GradientBoostingRegressor
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from sklearn.metrics import mean_squared_error, r2_score
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# Split
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, test_size=0.2, random_state=42
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)
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# Preprocess and train
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scaler = StandardScaler()
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X_train_scaled = scaler.fit_transform(X_train)
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X_test_scaled = scaler.transform(X_test)
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model = GradientBoostingRegressor(n_estimators=100, random_state=42)
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model.fit(X_train_scaled, y_train)
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# Evaluate
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y_pred = model.predict(X_test_scaled)
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print(f"RMSE: {mean_squared_error(y_test, y_pred, squared=False):.3f}")
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print(f"R² Score: {r2_score(y_test, y_pred):.3f}")
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```
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### Cross-Validation
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```python
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from sklearn.model_selection import cross_val_score
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from sklearn.ensemble import RandomForestClassifier
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model = RandomForestClassifier(n_estimators=100, random_state=42)
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scores = cross_val_score(model, X, y, cv=5, scoring='accuracy')
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print(f"CV Accuracy: {scores.mean():.3f} (+/- {scores.std() * 2:.3f})")
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```
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### Complete Pipeline with Mixed Data Types
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```python
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from sklearn.pipeline import Pipeline
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from sklearn.compose import ColumnTransformer
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from sklearn.preprocessing import StandardScaler, OneHotEncoder
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from sklearn.impute import SimpleImputer
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from sklearn.ensemble import RandomForestClassifier
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# Define feature types
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numeric_features = ['age', 'income']
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categorical_features = ['gender', 'occupation']
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# Create preprocessing pipelines
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numeric_transformer = Pipeline([
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('imputer', SimpleImputer(strategy='median')),
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('scaler', StandardScaler())
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])
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categorical_transformer = Pipeline([
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('imputer', SimpleImputer(strategy='most_frequent')),
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('onehot', OneHotEncoder(handle_unknown='ignore'))
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])
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# Combine transformers
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preprocessor = ColumnTransformer([
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('num', numeric_transformer, numeric_features),
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('cat', categorical_transformer, categorical_features)
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])
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# Full pipeline
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model = Pipeline([
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('preprocessor', preprocessor),
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('classifier', RandomForestClassifier(n_estimators=100, random_state=42))
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])
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# Fit and predict
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model.fit(X_train, y_train)
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y_pred = model.predict(X_test)
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```
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### Hyperparameter Tuning
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```python
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from sklearn.model_selection import GridSearchCV
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from sklearn.ensemble import RandomForestClassifier
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param_grid = {
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'n_estimators': [100, 200, 300],
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'max_depth': [10, 20, None],
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'min_samples_split': [2, 5, 10]
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}
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model = RandomForestClassifier(random_state=42)
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grid_search = GridSearchCV(
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model, param_grid, cv=5, scoring='accuracy', n_jobs=-1
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)
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grid_search.fit(X_train, y_train)
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print(f"Best params: {grid_search.best_params_}")
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print(f"Best score: {grid_search.best_score_:.3f}")
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# Use best model
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best_model = grid_search.best_estimator_
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```
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## Common Patterns
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### Loading Data
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```python
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# From scikit-learn datasets
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from sklearn.datasets import load_iris, load_digits, make_classification
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# Built-in datasets
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iris = load_iris()
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X, y = iris.data, iris.target
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# Synthetic data
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X, y = make_classification(
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n_samples=1000, n_features=20, n_classes=2, random_state=42
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)
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# From pandas
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import pandas as pd
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df = pd.read_csv('data.csv')
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X = df.drop('target', axis=1)
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y = df['target']
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```
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### Handling Imbalanced Data
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```python
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from sklearn.ensemble import RandomForestClassifier
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# Use class_weight parameter
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model = RandomForestClassifier(class_weight='balanced', random_state=42)
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model.fit(X_train, y_train)
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# Or use appropriate metrics
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from sklearn.metrics import balanced_accuracy_score, f1_score
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print(f"Balanced Accuracy: {balanced_accuracy_score(y_test, y_pred):.3f}")
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print(f"F1 Score: {f1_score(y_test, y_pred):.3f}")
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```
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### Feature Importance
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```python
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from sklearn.ensemble import RandomForestClassifier
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import pandas as pd
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model = RandomForestClassifier(n_estimators=100, random_state=42)
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model.fit(X_train, y_train)
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# Get feature importances
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importances = pd.DataFrame({
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'feature': feature_names,
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'importance': model.feature_importances_
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}).sort_values('importance', ascending=False)
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print(importances.head(10))
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```
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### Clustering
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```python
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from sklearn.cluster import KMeans
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from sklearn.preprocessing import StandardScaler
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# Scale data first
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scaler = StandardScaler()
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X_scaled = scaler.fit_transform(X)
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# Fit K-Means
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kmeans = KMeans(n_clusters=3, random_state=42)
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labels = kmeans.fit_predict(X_scaled)
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# Evaluate
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from sklearn.metrics import silhouette_score
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score = silhouette_score(X_scaled, labels)
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print(f"Silhouette Score: {score:.3f}")
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```
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### Dimensionality Reduction
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```python
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from sklearn.decomposition import PCA
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import matplotlib.pyplot as plt
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# Fit PCA
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pca = PCA(n_components=2)
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X_reduced = pca.fit_transform(X)
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# Plot
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plt.scatter(X_reduced[:, 0], X_reduced[:, 1], c=y, cmap='viridis')
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plt.xlabel('PC1')
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plt.ylabel('PC2')
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plt.title(f'PCA (explained variance: {pca.explained_variance_ratio_.sum():.2%})')
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```
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### Model Persistence
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```python
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import joblib
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# Save model
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joblib.dump(model, 'model.pkl')
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# Load model
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loaded_model = joblib.load('model.pkl')
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predictions = loaded_model.predict(X_new)
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```
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## Common Gotchas and Solutions
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### Data Leakage
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```python
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# WRONG: Fitting scaler on all data
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scaler = StandardScaler()
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X_scaled = scaler.fit_transform(X)
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X_train, X_test = train_test_split(X_scaled)
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# RIGHT: Fit on training data only
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X_train, X_test = train_test_split(X)
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scaler = StandardScaler()
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X_train_scaled = scaler.fit_transform(X_train)
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X_test_scaled = scaler.transform(X_test)
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# BEST: Use Pipeline
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from sklearn.pipeline import Pipeline
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pipeline = Pipeline([
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('scaler', StandardScaler()),
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('model', LogisticRegression())
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])
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pipeline.fit(X_train, y_train) # No leakage!
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```
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### Stratified Splitting for Classification
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```python
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# Always use stratify for classification
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, test_size=0.2, stratify=y, random_state=42
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)
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```
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### Random State for Reproducibility
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```python
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# Set random_state for reproducibility
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model = RandomForestClassifier(n_estimators=100, random_state=42)
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```
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### Handling Unknown Categories
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```python
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# Use handle_unknown='ignore' for OneHotEncoder
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encoder = OneHotEncoder(handle_unknown='ignore')
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```
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### Feature Names with Pipelines
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```python
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# Get feature names after transformation
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preprocessor.fit(X_train)
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feature_names = preprocessor.get_feature_names_out()
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```
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## Cheat Sheet: Algorithm Selection
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### Classification
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| Problem | Algorithm | When to Use |
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|---------|-----------|-------------|
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| Binary/Multiclass | Logistic Regression | Fast baseline, interpretability |
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| Binary/Multiclass | Random Forest | Good default, robust |
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| Binary/Multiclass | Gradient Boosting | Best accuracy, willing to tune |
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| Binary/Multiclass | SVM | Small data, complex boundaries |
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| Binary/Multiclass | Naive Bayes | Text classification, fast |
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| High dimensions | Linear SVM or Logistic | Text, many features |
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### Regression
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| Problem | Algorithm | When to Use |
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|---------|-----------|-------------|
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| Continuous target | Linear Regression | Fast baseline, interpretability |
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| Continuous target | Ridge/Lasso | Regularization needed |
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| Continuous target | Random Forest | Good default, non-linear |
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| Continuous target | Gradient Boosting | Best accuracy |
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| Continuous target | SVR | Small data, non-linear |
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### Clustering
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| Problem | Algorithm | When to Use |
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|---------|-----------|-------------|
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| Known K, spherical | K-Means | Fast, simple |
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| Unknown K, arbitrary shapes | DBSCAN | Noise/outliers present |
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| Hierarchical structure | Agglomerative | Need dendrogram |
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| Soft clustering | Gaussian Mixture | Probability estimates |
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### Dimensionality Reduction
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| Problem | Algorithm | When to Use |
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|---------|-----------|-------------|
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| Linear reduction | PCA | Variance explanation |
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| Visualization | t-SNE | 2D/3D plots |
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| Non-negative data | NMF | Images, text |
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| Sparse data | TruncatedSVD | Text, recommender systems |
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## Performance Tips
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### Speed Up Training
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```python
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# Use n_jobs=-1 for parallel processing
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model = RandomForestClassifier(n_estimators=100, n_jobs=-1)
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# Use warm_start for incremental learning
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model = RandomForestClassifier(n_estimators=100, warm_start=True)
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model.fit(X, y)
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model.n_estimators += 50
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model.fit(X, y) # Adds 50 more trees
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# Use partial_fit for online learning
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from sklearn.linear_model import SGDClassifier
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model = SGDClassifier()
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for X_batch, y_batch in batches:
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model.partial_fit(X_batch, y_batch, classes=np.unique(y))
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```
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### Memory Efficiency
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```python
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# Use sparse matrices
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from scipy.sparse import csr_matrix
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X_sparse = csr_matrix(X)
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# Use MiniBatchKMeans for large data
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from sklearn.cluster import MiniBatchKMeans
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model = MiniBatchKMeans(n_clusters=8, batch_size=100)
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```
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## Version Check
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```python
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import sklearn
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print(f"scikit-learn version: {sklearn.__version__}")
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```
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## Useful Resources
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- Official Documentation: https://scikit-learn.org/stable/
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- User Guide: https://scikit-learn.org/stable/user_guide.html
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- API Reference: https://scikit-learn.org/stable/api/index.html
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- Examples: https://scikit-learn.org/stable/auto_examples/index.html
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- Tutorials: https://scikit-learn.org/stable/tutorial/index.html
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