Train-Test Split and Data Preparation

What is Train Test Split?

Train Test Split means train-test split divides data into training data for learning and testing data for checking performance.

In real programs, this topic helps in testing on unseen data. Learn the idea first, then type the program yourself and compare the output.

Why should you learn this?

• It is useful for testing on unseen data.
• It connects with checking fair performance.
• It improves your ability to read, write and debug Python programs.

Important Terms

These terms are used directly in this lesson. Understand them before memorising the code.

Syntax / Basic Pattern

The simple pattern is: prepare data, apply the concept, then show the result.

from sklearn.model_selection import train_test_split
X = [[1], [2], [3], [4], [5]]
y = [40, 50, 60, 70, 80]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=1)
print("Train size:", len(X_train))
print("Test size:", len(X_test))

Complete Example Program

from sklearn.model_selection import train_test_split

X = [[1], [2], [3], [4], [5]]
y = [40, 50, 60, 70, 80]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=1)
print("Train size:", len(X_train))
print("Test size:", len(X_test))

Expected Output

Program Explanation

• from sklearn.model_selection import train_test_split imports ready-made features from a module/library.
• X = [[1], [2], [3], [4], [5]] stores a value in X.
• y = [40, 50, 60, 70, 80] stores a value in y.
• X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_ stores a value in X_train, X_test, y_train, y_test.
• print("Train size:", len(X_train)) displays information or calculated result on the screen.
• print("Test size:", len(X_test)) displays information or calculated result on the screen.

Where will you use it?

• Testing on unseen data.
• Checking fair performance.
• Avoiding overfitting.

Common Mistakes

• Training and testing the model on the same data.
• Using an algorithm without understanding the input features.
• Reporting only accuracy without checking actual mistakes and limitations.

Practice Tasks

1. Type the program in train-test-split.py and run it.
2. Change input values or sample data and observe the new output.
3. Create one example related to testing on unseen data.
4. Write 5 lines explaining the logic in your own words.

Summary

Train Test Split is not a theory-only topic. You should be able to explain the meaning, write the example, run it successfully, and use it in a small practical program.