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Scikit-learn

Biblioteca ML de Python - clasificacion, regresion, clustering, preprocesamiento, pipelines

TL;DR

Qué: La biblioteca de machine learning más popular de Python para ML clásico.

Por qué: API simple, algoritmos completos, excelente documentación, herramientas de preprocesamiento.

Quick Start

Instalar:

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

Tipo de algoritmoClases
ClasificaciónLogisticRegression, SVC, RandomForestClassifier
RegresiónLinearRegression, SVR, RandomForestRegressor
ClusteringKMeans, DBSCAN, AgglomerativeClustering
DimensionalidadPCA, TSNE, LDA
PreprocesamientoStandardScaler, MinMaxScaler, LabelEncoder

Gotchas

Preprocesamiento

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)

Validación cruzada

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}")

Evaluación del modelo

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}")

Algoritmos comunes

# 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