Stratified Sampling_1

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Stratified Sampling with Scikit-Learn

python

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from sklearn.model_selection import train_test_split
import pandas as pd
from collections import Counter

# Example dataset
data = {
    'Feature1': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100],
    'Feature2': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
    'Category': ['A', 'B', 'A', 'A', 'B', 'B', 'A', 'B', 'B', 'A']
}
df = pd.DataFrame(data)
# Perform stratified sampling
X = df[['Feature1', 'Feature2']]  # Features
y = df['Category']               # Target (strata)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, stratify=y, random_state=42
)
# Check distribution of the target variable
print("Original Distribution:", Counter(y))
print("Train Distribution:", Counter(y_train))
print("Test Distribution:", Counter(y_test))

Stratified Sampling with Custom Function

This code allows more control when you need stratified sampling on a pandas DataFrame:

python

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import pandas as pd

# Example dataset
data = {
    'Feature1': [10, 20, 30, 40, 50…