How do you split data into train and test?

 

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Splitting data into train and test sets is essential to evaluate a machine learning model’s performance on unseen data.

Purpose:

• Training set: Used to teach the model patterns in the data.

• Test set: Used to check how well the model generalizes to new data.

Common Steps:

1. Import Required Library

from sklearn.model_selection import train_test_split

2. Split the Data

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

• X = features, y = target labels.

• test_size=0.2 means 20% data for testing, 80% for training.

• random_state ensures reproducibility.

3. Optional – Validation Split

• Sometimes data is split into train, validation, and test sets (e.g., 70/15/15) for model tuning.

Best Practices:

• Shuffle the data before splitting to avoid order bias.

• Keep the test set separate until the final evaluation.

• For imbalanced data, use stratify=y to maintain class proportions.

This ensures the model is trained on one set and evaluated fairly on another.

Read More :

What is the difference between supervised and unsupervised learning?

What are the steps in building a machine learning model?

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