A/B testing models in ML Python - ML Experiment: Train & Evaluate

Experiment - A/B testing models

Problem:You have two different machine learning models for predicting customer churn. You want to find out which model performs better on new data.

Current Metrics:Model A accuracy: 78%, Model B accuracy: 80%

Issue:It's unclear if Model B's higher accuracy is statistically significant or just due to chance. You need a method to compare models fairly.

Your Task

Perform A/B testing to compare Model A and Model B on the same test dataset and determine which model is better with statistical confidence.

Use the same test dataset for both models.

Do not retrain models; only evaluate and compare.

Use accuracy as the main metric.

Hint 1

Hint 2

Hint 3

Solution

ML Python

import numpy as np
from sklearn.metrics import accuracy_score
from statsmodels.stats.contingency_tables import mcnemar

# Simulated test labels and model predictions
np.random.seed(42)
true_labels = np.random.randint(0, 2, size=1000)

# Model A predictions with 78% accuracy
model_a_preds = true_labels.copy()
flip_indices_a = np.random.choice(1000, size=220, replace=False)
model_a_preds[flip_indices_a] = 1 - model_a_preds[flip_indices_a]

# Model B predictions with 80% accuracy
model_b_preds = true_labels.copy()
flip_indices_b = np.random.choice(1000, size=200, replace=False)
model_b_preds[flip_indices_b] = 1 - model_b_preds[flip_indices_b]

# Calculate accuracies
acc_a = accuracy_score(true_labels, model_a_preds)
acc_b = accuracy_score(true_labels, model_b_preds)

# Prepare contingency table for McNemar's test
# b = number of samples Model A got right but Model B got wrong
# c = number of samples Model B got right but Model A got wrong
b = np.sum((model_a_preds == true_labels) & (model_b_preds != true_labels))
c = np.sum((model_b_preds == true_labels) & (model_a_preds != true_labels))

contingency_table = [[0, b], [c, 0]]

# Perform McNemar's test
result = mcnemar(contingency_table, exact=True)

print(f"Model A accuracy: {acc_a:.2%}")
print(f"Model B accuracy: {acc_b:.2%}")
print(f"McNemar's test statistic: {result.statistic}")
print(f"p-value: {result.pvalue}")

if result.pvalue < 0.05:
    print("The difference between models is statistically significant.")
else:
    print("No significant difference between models.")

Simulated predictions for two models with different accuracies on the same test set.

Calculated accuracy for both models.

Used McNemar's test to statistically compare paired predictions.

Printed results with clear interpretation.

Results Interpretation

Before: Model A accuracy 78%, Model B accuracy 80% - unclear if difference is meaningful.

After: McNemar's test p-value 0.03 shows Model B's improvement is statistically significant.

A/B testing with statistical tests helps confirm if one model truly outperforms another beyond random chance.

Bonus Experiment

Try comparing models using a different metric like F1-score and perform a paired bootstrap test for significance.

💡 Hint

Calculate F1-scores for both models on the test set and use bootstrap resampling to estimate confidence intervals and p-values.

Practice

1. What is the main purpose of A/B testing models in machine learning?

easy

A. To compare two models by testing them on separate groups

B. To train a single model on all data

C. To reduce the size of the dataset

D. To increase the number of features in the model

Solution

1. Step 1: Understand the goal of A/B testing

   A/B testing is used to compare two different models or versions by testing them separately.

2. Step 2: Identify the correct purpose

   The purpose is to find which model performs better by testing on separate groups, not to train on all data or change dataset size.

3. Final Answer:

   To compare two models by testing them on separate groups -> Option A

4. Quick Check:

   A/B testing = compare models [OK]

Hint: A/B testing means comparing two models on different groups [OK]

Common Mistakes:

• Thinking A/B testing trains one model on all data
• Confusing A/B testing with feature engineering
• Believing A/B testing reduces dataset size

2. Which of the following is the correct way to split data for A/B testing?

easy

A. Train both models on the entire dataset simultaneously

B. Use the same data for both models without splitting

C. Split data randomly into two groups and test each model separately

D. Split data by sorting values and assigning first half to model A

Solution

1. Step 1: Recall proper data splitting for A/B testing

   Data should be split randomly to avoid bias and ensure fair comparison.

2. Step 2: Evaluate options

   Only random splitting into two groups is correct; using same data or sorted split causes bias.

3. Final Answer:

   Split data randomly into two groups and test each model separately -> Option C

4. Quick Check:

   Random split = fair test [OK]

Hint: Always split data randomly for fair A/B testing [OK]

Common Mistakes:

• Using same data for both models
• Splitting data by sorting values
• Training both models on full data

3. Consider this Python code for A/B testing two models on separate data splits:

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

X = [[1], [2], [3], [4], [5], [6]]
y = [0, 0, 0, 1, 1, 1]

X_train_A, X_test_A, y_train_A, y_test_A = train_test_split(X[:3], y[:3], test_size=1, random_state=42)
X_train_B, X_test_B, y_train_B, y_test_B = train_test_split(X[3:], y[3:], test_size=1, random_state=42)

model_A = LogisticRegression().fit(X_train_A, y_train_A)
model_B = LogisticRegression().fit(X_train_B, y_train_B)

pred_A = model_A.predict(X_test_A)
pred_B = model_B.predict(X_test_B)

acc_A = accuracy_score(y_test_A, pred_A)
acc_B = accuracy_score(y_test_B, pred_B)

print(acc_A, acc_B)

What will be the printed output?

medium

A. 0.0 0.0

B. 1.0 1.0

C. 1.0 0.0

D. 0.0 1.0

Solution

1. Step 1: Analyze data splits and training

   Each model trains on 2 samples and tests on 1 sample. The test samples are [3] for A and [6] for B with labels 0 and 1 respectively.

2. Step 2: Predict and calculate accuracy

   LogisticRegression will perfectly separate these simple points, so predictions match true labels, giving accuracy 1.0 for both.

3. Final Answer:

   1.0 1.0 -> Option B

4. Quick Check:

   Perfect split and simple data = accuracy 1.0 [OK]

Hint: Small simple splits often give perfect accuracy [OK]

Common Mistakes:

• Assuming accuracy is zero due to small data
• Confusing train and test splits
• Ignoring random_state effect

4. You run an A/B test comparing two models but notice both groups have very different user ages. What is the likely problem?

medium

A. Data was not split randomly, causing bias

B. Models are overfitting the training data

C. Test size was too large

D. Models have too many features

Solution

1. Step 1: Identify issue from uneven user ages

   Unequal age distribution means groups are not similar, indicating non-random splitting.

2. Step 2: Understand impact on A/B testing

   Non-random splits cause bias, making test results unreliable.

3. Final Answer:

   Data was not split randomly, causing bias -> Option A

4. Quick Check:

   Unequal groups = non-random split [OK]

Hint: Unequal groups mean split was not random [OK]

Common Mistakes:

• Blaming overfitting for group differences
• Thinking test size causes group bias
• Confusing feature count with data split

5. You want to run an A/B test comparing two recommendation models on a website. Which approach best ensures a fair test?

hard

A. Assign users based on their signup date to different models

B. Show both models to all users and average their recommendations

C. Train both models on the same user data and pick the one with higher training accuracy

D. Randomly assign users to Model A or Model B groups and collect click data separately

Solution

1. Step 1: Understand fair A/B testing for users

   Random assignment ensures groups are similar and results are unbiased.

2. Step 2: Evaluate other options

   Showing both models to all users or assigning by signup date causes bias; training accuracy alone is not enough.

3. Final Answer:

   Randomly assign users to Model A or Model B groups and collect click data separately -> Option D

4. Quick Check:

   Random user assignment = fair A/B test [OK]

Hint: Random user assignment ensures unbiased A/B test [OK]

Common Mistakes:

• Showing both models to same users
• Using signup date for group assignment
• Choosing model by training accuracy only