Scikit-learn

Machine learning in Python.

Quick Start

from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error, r2_score

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

model = RandomForestRegressor(n_estimators=100)
model.fit(X_train, y_train)
predictions = model.predict(X_test)

Key Models

# Regression
from sklearn.linear_model import Ridge, Lasso
from sklearn.ensemble import GradientBoostingRegressor

# Classification
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC

# Clustering
from sklearn.cluster import KMeans

Preprocessing

from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.pipeline import Pipeline

pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('model', RandomForestRegressor())
])
pipeline.fit(X_train, y_train)

Cross-validation

from sklearn.model_selection import cross_val_score
scores = cross_val_score(model, X, y, cv=5, scoring='r2')