Does the pipeline approach with StandardScaler generalize to tree-based ensembles or neural networks?

Question

I’m using a Pipeline in scikit-learn to combine feature scaling with a classifier. This works well for logistic regression, but I’m curious if this approach would generalize effectively to more complex models like tree-based ensembles or neural networks. Specifically, do these models require different scaling strategies, or can I apply StandardScaler consistently across them?

import numpy as np
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.ensemble import RandomForestClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.metrics import accuracy_score

# Generate sample data
np.random.seed(42)
X = np.random.rand(200, 5)  # 200 samples, 5 features
y = np.random.randint(0, 2, 200)  # Binary target

# Split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Define pipelines for different models
pipelines = {
    'logistic_regression': Pipeline([
        ('scaler', StandardScaler()),
        ('classifier', LogisticRegression())
    ]),
    'random_forest': Pipeline([
        ('scaler', StandardScaler()),
        ('classifier', RandomForestClassifier())
    ]),
    'neural_network': Pipeline([
        ('scaler', StandardScaler()),
        ('classifier', MLPClassifier(max_iter=500))
    ])
}

# Evaluate each model
for model_name, pipeline in pipelines.items():
    pipeline.fit(X_train, y_train)
    y_pred = pipeline.predict(X_test)
    print(f"{model_name} Accuracy: {accuracy_score(y_test, y_pred)}")

Does the pipeline approach with StandardScaler generalize to tree-based ensembles or neural networks?

Answers (1)

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