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Scikit-learn

Python classical ML library - simple API, comprehensive algorithms, preprocessing, pipelines

TL;DR

What: Python’s most popular machine learning library for classical ML.

Why: Simple API, comprehensive algorithms, excellent documentation, preprocessing tools.

Quick Start

Install:

pip install scikit-learn

Hello Scikit-learn:

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# Load data
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(
    iris.data, iris.target, test_size=0.2, random_state=42
)

# Train
model = RandomForestClassifier()
model.fit(X_train, y_train)

# Predict
predictions = model.predict(X_test)
print(f"Accuracy: {accuracy_score(y_test, predictions):.2f}")

Cheatsheet

Algorithm TypeClasses
ClassificationLogisticRegression, SVC, RandomForestClassifier
RegressionLinearRegression, SVR, RandomForestRegressor
ClusteringKMeans, DBSCAN, AgglomerativeClustering
DimensionalityPCA, TSNE, LDA
PreprocessingStandardScaler, MinMaxScaler, LabelEncoder

Gotchas

Preprocessing

from sklearn.preprocessing import StandardScaler, LabelEncoder, OneHotEncoder
from sklearn.impute import SimpleImputer

# Numerical scaling
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# Label encoding
encoder = LabelEncoder()
y_encoded = encoder.fit_transform(['cat', 'dog', 'cat'])

# One-hot encoding
onehot = OneHotEncoder(sparse_output=False)
X_onehot = onehot.fit_transform(X_categorical)

# Handle missing values
imputer = SimpleImputer(strategy='mean')
X_imputed = imputer.fit_transform(X)

Pipelines

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC

pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('svm', SVC(kernel='rbf'))
])

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

Cross-validation

from sklearn.model_selection import cross_val_score, GridSearchCV

# Simple cross-validation
scores = cross_val_score(model, X, y, cv=5)
print(f"Mean accuracy: {scores.mean():.2f} (+/- {scores.std() * 2:.2f})")

# Grid search
param_grid = {
    'n_estimators': [50, 100, 200],
    'max_depth': [None, 10, 20]
}

grid_search = GridSearchCV(
    RandomForestClassifier(),
    param_grid,
    cv=5,
    scoring='accuracy'
)
grid_search.fit(X_train, y_train)

print(f"Best params: {grid_search.best_params_}")
print(f"Best score: {grid_search.best_score_:.2f}")

Model evaluation

from sklearn.metrics import (
    classification_report, confusion_matrix,
    mean_squared_error, r2_score
)

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

# Regression
mse = mean_squared_error(y_test, predictions)
r2 = r2_score(y_test, predictions)
print(f"MSE: {mse:.2f}, R2: {r2:.2f}")

Common algorithms

# Classification
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB

# Regression
from sklearn.linear_model import Ridge, Lasso
from sklearn.tree import DecisionTreeRegressor

# Clustering
from sklearn.cluster import KMeans, DBSCAN

Next Steps