Train-test split in ML Python - Model Pipeline Trace

Model Pipeline - Train-test split

The train-test split is a simple way to divide data into two parts: one for training the model and one for testing how well it learned. This helps us check if the model can make good predictions on new data.

Data Flow - 2 Stages

1Original dataset

1000 rows x 5 columns→Starting data with features and labels→1000 rows x 5 columns

[[5.1, 3.5, 1.4, 0.2, 'setosa'], ...]

↓

2Train-test split

1000 rows x 5 columns→Split data into 80% training and 20% testing randomly→Train: 800 rows x 5 columns, Test: 200 rows x 5 columns

Train example: [[5.0, 3.6, 1.4, 0.2, 'setosa'], ...], Test example: [[6.7, 3.1, 4.7, 1.5, 'versicolor'], ...]

Training Trace - Epoch by Epoch

Loss
0.7 |****
0.6 |*** 
0.5 |**  
0.4 |*   
0.3 |*   
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.60	Model starts learning with moderate loss and accuracy
2	0.50	0.75	Loss decreases and accuracy improves as model learns
3	0.40	0.82	Model continues to improve with lower loss and higher accuracy
4	0.35	0.86	Training converges with steady improvement
5	0.30	0.89	Final epoch shows good performance on training data

Prediction Trace - 4 Layers

Layer 1: Input sample from test set

Layer 2: Feature scaling (if applied)

Layer 3: Model prediction

Layer 4: Class selection

Model Quiz - 3 Questions

What does the train-test split stage do to the data?

A Removes half of the data

B Combines training and testing data

C Divides data into training and testing parts

D Changes the data labels

Key Insight

Splitting data into training and testing sets helps us check if the model learns well and can predict new data accurately. Watching loss go down and accuracy go up during training shows the model is improving.

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