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Scikit-learn Reference Guide

Complete reference for Scikit-learn - the most popular machine learning library in Python.

Installation and Import

Installation

pip install scikit-learn

Import

from sklearn import <module>
# Examples:
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression

Data Splitting

traintestsplit()

Description: Split arrays into train and test subsets

Syntax: traintestsplit(*arrays, testsize=0.25, randomstate=None)

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Stratified split (maintains class distribution)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, stratify=y, random_state=42
)

Preprocessing

StandardScaler

Description: Standardize features (mean=0, std=1)

Methods: fit(), transform(), fit_transform()

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

MinMaxScaler

Description: Scale features to [0, 1] range

Methods: fit(), transform(), fit_transform()

from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()
X_scaled = scaler.fit_transform(X)

LabelEncoder

Description: Encode categorical labels as integers

Methods: fit(), transform(), fit_transform()

from sklearn.preprocessing import LabelEncoder

encoder = LabelEncoder()
y_encoded = encoder.fit_transform(y)
y_decoded = encoder.inverse_transform(y_encoded)

OneHotEncoder

Description: Encode categorical features as one-hot vectors

Methods: fit(), transform(), fit_transform()

from sklearn.preprocessing import OneHotEncoder

encoder = OneHotEncoder(sparse=False)
X_encoded = encoder.fit_transform(X)

Regression Models

LinearRegression

Description: Ordinary least squares linear regression

Methods: fit(), predict(), score()

from sklearn.linear_model import LinearRegression

model = LinearRegression()
model.fit(X_train, y_train)
predictions = model.predict(X_test)
score = model.score(X_test, y_test)  # R² score

# Get coefficients
print(model.coef_)
print(model.intercept_)

Ridge

Description: Linear regression with L2 regularization

Parameters: alpha (regularization strength)

from sklearn.linear_model import Ridge

model = Ridge(alpha=1.0)
model.fit(X_train, y_train)
predictions = model.predict(X_test)

Lasso

Description: Linear regression with L1 regularization

Parameters: alpha (regularization strength)

from sklearn.linear_model import Lasso

model = Lasso(alpha=0.1)
model.fit(X_train, y_train)
predictions = model.predict(X_test)

Classification Models

LogisticRegression

Description: Logistic regression classifier

Parameters: C (inverse regularization strength)

from sklearn.linear_model import LogisticRegression

model = LogisticRegression()
model.fit(X_train, y_train)
predictions = model.predict(X_test)
probabilities = model.predict_proba(X_test)

DecisionTreeClassifier

Description: Decision tree classifier

Parameters: maxdepth, minsamples_split

from sklearn.tree import DecisionTreeClassifier

model = DecisionTreeClassifier(max_depth=5, random_state=42)
model.fit(X_train, y_train)
predictions = model.predict(X_test)

# Feature importance
print(model.feature_importances_)

RandomForestClassifier

Description: Random forest ensemble classifier

Parameters: nestimators, maxdepth

from sklearn.ensemble import RandomForestClassifier

model = RandomForestClassifier(n_estimators=100, max_depth=10, random_state=42)
model.fit(X_train, y_train)
predictions = model.predict(X_test)

SVC (Support Vector Classifier)

Description: Support Vector Machine classifier

Parameters: kernel, C, gamma

from sklearn.svm import SVC

model = SVC(kernel='rbf', C=1.0, gamma='scale')
model.fit(X_train, y_train)
predictions = model.predict(X_test)

KNeighborsClassifier

Description: K-Nearest Neighbors classifier

Parameters: n_neighbors

from sklearn.neighbors import KNeighborsClassifier

model = KNeighborsClassifier(n_neighbors=5)
model.fit(X_train, y_train)
predictions = model.predict(X_test)

GradientBoostingClassifier

Description: Gradient boosting classifier

Parameters: nestimators, learningrate

from sklearn.ensemble import GradientBoostingClassifier

model = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1)
model.fit(X_train, y_train)
predictions = model.predict(X_test)

Clustering

KMeans

Description: K-Means clustering

Parameters: n_clusters

from sklearn.cluster import KMeans

kmeans = KMeans(n_clusters=3, random_state=42)
clusters = kmeans.fit_predict(X)
centers = kmeans.cluster_centers_

DBSCAN

Description: Density-based clustering

Parameters: eps, min_samples

from sklearn.cluster import DBSCAN

dbscan = DBSCAN(eps=0.5, min_samples=5)
clusters = dbscan.fit_predict(X)

Model Evaluation

Regression Metrics

from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import numpy as np

# Mean Squared Error
mse = mean_squared_error(y_test, predictions)

# Root Mean Squared Error
rmse = np.sqrt(mse)

# Mean Absolute Error
mae = mean_absolute_error(y_test, predictions)

# R² Score
r2 = r2_score(y_test, predictions)

Classification Metrics

from sklearn.metrics import (
    accuracy_score, precision_score, recall_score, f1_score,
    confusion_matrix, classification_report
)

# Accuracy
accuracy = accuracy_score(y_test, predictions)

# Precision
precision = precision_score(y_test, predictions, average='binary')

# Recall
recall = recall_score(y_test, predictions, average='binary')

# F1 Score
f1 = f1_score(y_test, predictions, average='binary')

# Confusion Matrix
cm = confusion_matrix(y_test, predictions)

# Classification Report
print(classification_report(y_test, predictions))

Cross-Validation

crossvalscore()

Description: Evaluate model using cross-validation

Syntax: crossvalscore(estimator, X, y, cv=5)

from sklearn.model_selection import cross_val_score

scores = cross_val_score(model, X, y, cv=5)
print(f"Mean score: {scores.mean()}")
print(f"Std: {scores.std()}")

cross_validate()

Description: Cross-validation with multiple metrics

Syntax: cross_validate(estimator, X, y, cv=5, scoring=None)

from sklearn.model_selection import cross_validate

scoring = ['accuracy', 'precision', 'recall']
scores = cross_validate(model, X, y, cv=5, scoring=scoring)

Hyperparameter Tuning

GridSearchCV

Description: Exhaustive search over parameter grid

Syntax: GridSearchCV(estimator, param_grid, cv=5)

from sklearn.model_selection import GridSearchCV

param_grid = {
    'n_estimators': [50, 100, 200],
    'max_depth': [5, 10, 15, None]
}

grid_search = GridSearchCV(
    RandomForestClassifier(),
    param_grid,
    cv=5,
    scoring='accuracy'
)

grid_search.fit(X_train, y_train)
best_model = grid_search.best_estimator_
best_params = grid_search.best_params_
best_score = grid_search.best_score_

RandomizedSearchCV

Description: Random search over parameter distributions

Syntax: RandomizedSearchCV(estimator, paramdistributions, niter=10, cv=5)

from sklearn.model_selection import RandomizedSearchCV
from scipy.stats import randint

param_dist = {
    'n_estimators': randint(50, 200),
    'max_depth': randint(5, 20)
}

random_search = RandomizedSearchCV(
    RandomForestClassifier(),
    param_dist,
    n_iter=20,
    cv=5,
    random_state=42
)

random_search.fit(X_train, y_train)
best_model = random_search.best_estimator_

Pipelines

Pipeline

Description: Chain transformers and estimator

Syntax: Pipeline(steps)

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression

pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('classifier', LogisticRegression())
])

pipeline.fit(X_train, y_train)
predictions = pipeline.predict(X_test)

Model Persistence

Save Model

import joblib

# Save
joblib.dump(model, 'model.pkl')

# Load
loaded_model = joblib.load('model.pkl')

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