Optimizing Your Workflow: 30 Handy scikit-learn Tricks

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scikit-learn (sklearn) is a powerful machine learning library in Python. Here are some tricks and techniques that can help you use scikit-learn more effectively and elegantly:

1. Pipeline for Preprocessing and Modeling:

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

model = make_pipeline(StandardScaler(), SVC())
model.fit(X_train, y_train)

2. Grid Search for Hyperparameter Tuning:

from sklearn.model_selection import GridSearchCV

param_grid = {'C': [0.1, 1, 10], 'kernel': ['linear', 'rbf']}
grid_search = GridSearchCV(SVC(), param_grid, cv=5)
grid_search.fit(X_train, y_train)

3. Train-Test Split:

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)

4. Cross-Validation Scoring:

from sklearn.model_selection import cross_val_score

scores = cross_val_score(model, X, y, cv=5)

5. Feature Scaling with StandardScaler:

from sklearn.preprocessing import…