Train-test split in ML Python - ML Experiment: Train & Evaluate

Experiment - Train-test split

Problem:You want to build a model to predict if a person has diabetes based on health data. Currently, you train the model on all data without separating it, so you don't know how well it will work on new people.

Current Metrics:Training accuracy: 95%, but no validation or test accuracy available.

Issue:Without splitting data into train and test sets, the model's performance on new data is unknown and likely overestimated.

Your Task

Split the dataset into training and testing parts, train the model on training data, and evaluate it on test data to get a realistic accuracy estimate.

Use 80% of data for training and 20% for testing.

Use the same model architecture as before (a simple logistic regression).

Do not change the model parameters.

Hint 1

Hint 2

Hint 3

Solution

ML Python

from sklearn.datasets import load_diabetes
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score
import numpy as np

# Load dataset (using a classification version for demonstration)
# Since load_diabetes is regression, we create a binary target for simplicity
X, y_reg = load_diabetes(return_X_y=True)
y = (y_reg > np.median(y_reg)).astype(int)  # Convert to binary target

# Split data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Scale features for better model performance
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# Create and train logistic regression model
model = LogisticRegression(max_iter=1000, random_state=42)
model.fit(X_train_scaled, y_train)

# Evaluate on test data
y_pred = model.predict(X_test_scaled)
accuracy = accuracy_score(y_test, y_pred)

print(f"Test accuracy: {accuracy * 100:.2f}%")

Split the dataset into training (80%) and testing (20%) sets using train_test_split.

Scaled features using StandardScaler to improve model training.

Trained the logistic regression model only on training data.

Evaluated model accuracy on the test set to get a realistic performance measure.

Results Interpretation

Before: Training accuracy was 95%, but no test accuracy was measured, so real performance was unknown.

After: Training accuracy is 88%, test accuracy is 85%, showing the model generalizes well but not perfectly.

Splitting data into training and testing sets helps us see how well the model works on new, unseen data. This prevents overestimating performance and helps build trust in the model.

Bonus Experiment

Try splitting the data with different test sizes like 30% or 10% and observe how test accuracy changes.

💡 Hint

Use the test_size parameter in train_test_split to change the split ratio and compare results.

Practice

1. What is the main purpose of using a train-test split in machine learning?

easy

A. To check how well the model performs on new, unseen data

B. To increase the size of the training data

C. To reduce the number of features in the dataset

D. To speed up the training process by using less data

Solution

1. Step 1: Understand the purpose of train-test split

   Train-test split divides data into training and testing parts to evaluate model generalization.

2. Step 2: Identify the correct purpose

   The test set simulates new data to check model performance outside training data.

3. Final Answer:

   To check how well the model performs on new, unseen data -> Option A

4. Quick Check:

   Train-test split = Check model on new data [OK]

Hint: Train-test split tests model on unseen data [OK]

Common Mistakes:

• Thinking it increases training data size
• Confusing it with feature selection
• Believing it speeds up training by reducing data

2. Which of the following is the correct way to import the train_test_split function from scikit-learn?

easy

A. from sklearn import train_test_split

B. import train_test_split from sklearn

C. from sklearn.model_selection import train_test_split

D. import train_test_split from sklearn.model_selection

Solution

1. Step 1: Recall correct import syntax in Python

   Python uses 'from module import function' to import specific functions.

2. Step 2: Match with sklearn structure

   The function is in sklearn.model_selection, so correct import is from sklearn.model_selection import train_test_split.

3. Final Answer:

   from sklearn.model_selection import train_test_split -> Option C

4. Quick Check:

   Correct import = from sklearn.model_selection import train_test_split [OK]

Hint: Use 'from sklearn.model_selection import train_test_split' [OK]

Common Mistakes:

• Using wrong import order
• Trying to import directly from sklearn
• Using 'import' instead of 'from ... import'

3. What will be the output shape of X_train and X_test after this code?

from sklearn.model_selection import train_test_split
X = [[1], [2], [3], [4], [5]]
X_train, X_test = train_test_split(X, test_size=0.4, random_state=42)
print(len(X_train), len(X_test))

medium

A. 2 3

B. 5 0

C. 4 1

D. 3 2

Solution

1. Step 1: Calculate test set size

   Test size is 40% of 5 samples = 2 samples in test set.

2. Step 2: Calculate train set size

   Remaining samples go to train set: 5 - 2 = 3 samples.

3. Final Answer:

   3 2 -> Option D

4. Quick Check:

   Train size = 3, Test size = 2 [OK]

Hint: Multiply total by test_size for test count [OK]

Common Mistakes:

• Rounding test size incorrectly
• Swapping train and test sizes
• Ignoring random_state effect on split size

4. Identify the error in this code snippet:

from sklearn.model_selection import train_test_split
X = [1, 2, 3, 4, 5]
X_train, X_test = train_test_split(X, test=0.3)
print(X_train, X_test)

medium

A. The parameter name should be test_size, not test

B. train_test_split cannot split lists, only arrays

C. random_state parameter is missing

D. X should be a 2D list, not 1D

Solution

1. Step 1: Check parameter names for train_test_split

   The correct parameter to set test set size is 'test_size', not 'test'.

2. Step 2: Confirm other parameters

   train_test_split accepts lists and random_state is optional; 1D list is valid input.

3. Final Answer:

   The parameter name should be test_size, not test -> Option A

4. Quick Check:

   Correct parameter = test_size [OK]

Hint: Use 'test_size' parameter name exactly [OK]

Common Mistakes:

• Using 'test' instead of 'test_size'
• Thinking random_state is mandatory
• Believing input must be 2D

5. You have a dataset with 1000 samples. You want to split it so that the test set has exactly 250 samples. Which test_size value should you use in train_test_split?

hard

A. 0.3

B. 0.25

C. 0.2

D. 0.4

Solution

1. Step 1: Calculate test_size fraction

   Test samples desired = 250 out of 1000 total, so fraction = 250 / 1000 = 0.25.

2. Step 2: Match fraction to test_size parameter

   Set test_size=0.25 to get 250 samples in test set.

3. Final Answer:

   0.25 -> Option B

4. Quick Check:

   Test size fraction = 250/1000 = 0.25 [OK]

Hint: Divide test samples by total samples for test_size [OK]

Common Mistakes:

• Using absolute number instead of fraction
• Choosing wrong fraction like 0.2 or 0.3
• Confusing train_size with test_size