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Data splitting

Data is split into train and test sets with train_test_split.

stratify

If classes are imbalanced, a random split may leave few or none of one class in the test set. stratify=y keeps the class proportions in both sets:

X_tr, X_te, y_tr, y_te = train_test_split(
    X, y, test_size=0.2, stratify=y, random_state=0)

In classification problems stratify=y is almost always a good default.

Validation set

If you choose model settings (hyperparameters) by looking at the test set, the test set is indirectly "learned" too. That's why a three-way split is used:

X_tmp, X_test, y_tmp, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
X_train, X_val, y_train, y_val = train_test_split(X_tmp, y_tmp, test_size=0.25, random_state=0)
# result: 60% train, 20% validation, 20% test

Settings are chosen with the validation set; the test set is used only once, at the very end.

Practice Stratified split

Split using y made of 16 zeros and 4 ones with test_size=0.25, stratify=y and random_state=0. Print the sum of the test labels (the number of 1s).

Starter code

model.py
from sklearn.model_selection import train_test_split

X = [[i] for i in range(20)]
y = [0] * 16 + [1] * 4

# buraya yaz

Sample solution

model.py
from sklearn.model_selection import train_test_split

X = [[i] for i in range(20)]
y = [0] * 16 + [1] * 4

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, stratify=y, random_state=0)
print(sum(y_test))

Expected output

1

Questions and answers

Review the lesson questions together with their answers. To test yourself, solve the lesson interactively.

1 With labels that are 90% 0 and 10% 1, what is the share of 1s in the test set when <code>stratify=y</code> is used?

  • About 10% Correct answer
  • 50%
  • 0%
  • Random

Explanation: Stratified splitting keeps the original class proportion in each set.

2 Keep the class proportions in the split.

Question 
train_test_split(X, y, test_size=0.2, ____=y)
Answer 
train_test_split(X, y, test_size=0.2, stratify=y)

Explanation: stratify=y performs a stratified split by the class proportions of y.

3 Match the sets with their roles.

Term Meaning
Training The model learns
Validation Settings are chosen
Test Measured once at the end
stratify Keeps class proportion

All lessons in Beginner

1 What is ML?

  1. What is machine learning?
  2. Supervised and unsupervised
  3. Data and model
  4. Training and testing
  5. Feature and label notation
  6. Summary - ML fundamentals

2 NumPy basics

  1. What is NumPy?
  2. Creating arrays
  3. Array properties
  4. Array indexing
  5. Reshaping arrays
  6. Summary - NumPy basics

3 Pandas basics

  1. What is Pandas?
  2. Creating a DataFrame
  3. Column operations
  4. Selecting and filtering data
  5. Viewing first rows
  6. Summary - Pandas basics

4 Data preprocessing

  1. Data cleaning
  2. Missing data
  3. Data types
  4. Label encoding
  5. Data splitting
  6. Summary - Data preprocessing

5 Statistics basics

  1. Mean
  2. Median
  3. Standard deviation
  4. Variance
  5. Correlation
  6. Summary - Statistics

6 Linear algebra

  1. Vectors
  2. Matrices
  3. Matrix multiplication
  4. Transpose
  5. Identity matrix
  6. Summary - Linear algebra

7 Project: Data analysis

  1. Loading data
  2. Cleaning data
  3. Computing statistics
  4. Printing results

8 Regression

  1. What is regression?
  2. Linear regression
  3. Model training
  4. Prediction
  5. Error measurement
  6. Summary - Regression

9 KNN

  1. What is KNN?
  2. The K parameter
  3. Distance measurement
  4. Classification
  5. Model training
  6. Summary - KNN

10 Decision trees

  1. What is a decision tree?
  2. Splitting criterion
  3. Depth
  4. Classification
  5. Overfitting
  6. Summary - Decision trees

11 Classification

  1. What is classification?
  2. Binary classification
  3. Multi-class
  4. Accuracy
  5. Confusion matrix
  6. Summary - Classification

12 Model evaluation

  1. Train-test split
  2. Accuracy
  3. Precision
  4. Recall
  5. F1 score
  6. Summary - Model evaluation

13 Project: Classification

  1. Preparing data
  2. Training model
  3. Prediction
  4. Evaluation

14 Scaling

  1. What is scaling?
  2. StandardScaler
  3. MinMaxScaler
  4. When to use?
  5. Pipeline
  6. Summary - Scaling

15 Feature engineering

  1. What is a feature?
  2. Feature selection
  3. Feature extraction
  4. Categorical features
  5. Numerical features
  6. Summary - Feature engineering

16 Model selection

  1. What is model selection?
  2. Cross-validation
  3. Grid search
  4. Score comparison
  5. Hyperparameter
  6. Summary - Model selection

17 Project: Regression

  1. Preparing data
  2. Training model
  3. Prediction
  4. Error measurement

18 Metrics

  1. What is a metric?
  2. Accuracy
  3. Precision
  4. Recall
  5. F1 score
  6. Summary - Metrics

19 Data visualization

  1. What is visualization?
  2. Scatter plot
  3. Histogram
  4. Line plot
  5. Box plot
  6. Summary - Visualization

20 Beginner final

  1. Challenge 1
  2. Challenge 2
  3. Challenge 3
  4. Challenge 4