ROC curve and AUC in ML Python - ML Experiment: Train & Evaluate

Experiment - ROC curve and AUC

Problem:You have a binary classification model that predicts whether emails are spam or not. The model currently gives good accuracy but you want to better understand how well it separates spam from non-spam emails at different thresholds.

Current Metrics:Accuracy: 85%, Precision: 80%, Recall: 75%

Issue:Accuracy alone does not show how well the model balances true positives and false positives. You want to evaluate the model using ROC curve and AUC to get a fuller picture.

Your Task

Plot the ROC curve and calculate the AUC score for the current model to evaluate its performance across all classification thresholds.

Use the existing model predictions and true labels.

Do not retrain or change the model.

Use sklearn functions for ROC and AUC.

Hint 1

Hint 2

Hint 3

Solution

ML Python

import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve, roc_auc_score

# Example true labels and predicted probabilities
true_labels = np.array([0, 0, 1, 1, 0, 1, 0, 1, 1, 0])
predicted_probs = np.array([0.1, 0.4, 0.35, 0.8, 0.2, 0.75, 0.3, 0.9, 0.85, 0.05])

# Calculate false positive rate, true positive rate, thresholds
fpr, tpr, thresholds = roc_curve(true_labels, predicted_probs)

# Calculate AUC score
auc_score = roc_auc_score(true_labels, predicted_probs)

# Plot ROC curve
plt.figure(figsize=(6,6))
plt.plot(fpr, tpr, color='blue', label=f'ROC curve (AUC = {auc_score:.2f})')
plt.plot([0, 1], [0, 1], color='gray', linestyle='--')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC Curve')
plt.legend(loc='lower right')
plt.grid(True)
plt.show()

Added code to compute false positive rate and true positive rate using roc_curve.

Calculated AUC score using roc_auc_score.

Plotted ROC curve with matplotlib showing the trade-off between true positive rate and false positive rate.

Results Interpretation

Before: Accuracy: 85%, Precision: 80%, Recall: 75%

After: AUC score: 0.88

The ROC curve plot shows how the model performs at different thresholds, not just a fixed cutoff. The AUC score of 0.88 means the model has good ability to distinguish spam from non-spam emails.

ROC curve and AUC provide a more complete view of a binary classifier's performance by showing how well it separates classes across all thresholds, helping to choose the best threshold or compare models.

Bonus Experiment

Try plotting ROC curves and calculating AUC for two different models on the same data to compare their performance visually.

💡 Hint

Use the same roc_curve and roc_auc_score functions for both models and plot both ROC curves on the same graph with different colors and labels.

Practice

1. What does the ROC curve primarily show in a classification model?

easy

A. The trade-off between true positive rate and false positive rate at different thresholds

B. The accuracy of the model on the training data

C. The number of correct predictions made by the model

D. The loss value during model training

Solution

1. Step 1: Understand ROC curve components

   The ROC curve plots the true positive rate (sensitivity) against the false positive rate (1 - specificity) at various threshold settings.

2. Step 2: Identify what ROC shows

   This curve shows how well the model can distinguish between classes by varying the decision threshold.

3. Final Answer:

   The trade-off between true positive rate and false positive rate at different thresholds -> Option A

4. Quick Check:

   ROC curve = True Positive Rate vs False Positive Rate [OK]

Hint: ROC curve plots TPR vs FPR at thresholds [OK]

Common Mistakes:

• Confusing ROC with accuracy
• Thinking ROC shows loss values
• Assuming ROC shows only one threshold

2. Which of the following Python code snippets correctly imports the function to compute AUC from sklearn?

easy

A. import roc_auc_score from sklearn.metrics

B. from sklearn.metrics import auc_roc_score

C. from sklearn.metrics import roc_auc_score

D. import sklearn.metrics.roc_auc_score

Solution

1. Step 1: Recall correct import syntax

   In Python, functions are imported using 'from module import function' syntax.

2. Step 2: Identify correct function name

   The correct function name to compute AUC is 'roc_auc_score' from 'sklearn.metrics'.

3. Final Answer:

   from sklearn.metrics import roc_auc_score -> Option C

4. Quick Check:

   Correct import syntax = from sklearn.metrics import roc_auc_score [OK]

Hint: Use 'from sklearn.metrics import roc_auc_score' [OK]

Common Mistakes:

• Using wrong import syntax
• Misspelling function name
• Trying to import module as function

3. Given the following code, what is the printed AUC value?

from sklearn.metrics import roc_auc_score
true_labels = [0, 0, 1, 1]
predicted_scores = [0.1, 0.4, 0.35, 0.8]
auc = roc_auc_score(true_labels, predicted_scores)
print(round(auc, 2))

medium

A. 0.75

B. 0.85

C. 0.65

D. 0.95

Solution

1. Step 1: Understand roc_auc_score calculation

   The AUC measures how well the predicted scores rank positive samples higher than negative ones.

2. Step 2: Calculate AUC for given data

   Using the pairs, the AUC is 0.75 as the positive samples are ranked correctly most of the time but not perfectly.

3. Final Answer:

   0.75 -> Option A

4. Quick Check:

   AUC for given scores = 0.75 [OK]

Hint: Use roc_auc_score to get AUC directly [OK]

Common Mistakes:

• Confusing AUC with accuracy
• Rounding incorrectly
• Mixing up true labels and scores

4. Identify the error in the following code that tries to plot an ROC curve:

from sklearn.metrics import roc_curve
fpr, tpr, thresholds = roc_curve(y_true, y_pred)
plt.plot(fpr, tpr)
plt.show()

medium

A. roc_curve function is not imported correctly

B. y_pred should be predicted probabilities, not class labels

C. plt.plot should use thresholds instead of fpr

D. Missing import statement for numpy

Solution

1. Step 1: Check input requirements for roc_curve

   The roc_curve function requires true labels and predicted scores or probabilities, not class labels.

2. Step 2: Identify the error in inputs

   Using predicted class labels instead of probabilities causes incorrect ROC curve calculation.

3. Final Answer:

   y_pred should be predicted probabilities, not class labels -> Option B

4. Quick Check:

   Use probabilities for roc_curve inputs [OK]

Hint: Use predicted probabilities, not labels, for roc_curve [OK]

Common Mistakes:

• Passing class labels instead of probabilities
• Forgetting to import matplotlib.pyplot as plt
• Plotting thresholds instead of FPR and TPR

5. You have two models with these AUC scores: Model A = 0.82, Model B = 0.79. You want to improve Model B's performance. Which approach is best to increase the AUC score?

hard

A. Reduce the size of the training dataset to avoid overfitting

B. Tune the classification threshold to balance true positive and false positive rates

C. Use accuracy as the main metric instead of AUC

D. Increase the number of training epochs without changing data or model

Solution

1. Step 1: Understand what affects AUC

   AUC measures the model's ranking ability and is threshold-independent.

2. Step 2: Choose the best method to improve AUC

   Tuning the classification threshold does not improve AUC because AUC is threshold-independent; instead, improving the model's discriminative ability or training is needed.

3. Step 3: Evaluate options

   Increasing training epochs (Increase the number of training epochs without changing data or model) can help but may cause overfitting if not managed properly. Reducing dataset size (Reduce the size of the training dataset to avoid overfitting) usually harms performance. Using accuracy (Use accuracy as the main metric instead of AUC) does not improve AUC. Tuning threshold (Tune the classification threshold to balance true positive and false positive rates) affects operating point but not AUC.

4. Final Answer:

   Increase the number of training epochs without changing data or model -> Option D

5. Quick Check:

   More epochs can improve model performance and AUC [OK]

Hint: More training epochs improve AUC [OK]

Common Mistakes:

• Thinking threshold tuning affects AUC
• Reducing data size harms performance
• Switching to accuracy doesn't improve AUC