Model evaluation best practices in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the accuracy of a classification model.

Computer Vision

accuracy = accuracy_score(y_true, [1])

Drag options to blanks, or click blank then click option'

Ay_train

By_test

Cy_pred

Dy_val

Attempts:

3 left

💡 Hint

Common Mistakes

Using training labels instead of predicted labels.

Confusing true labels with predicted labels.

The accuracy_score function compares the true labels y_true with the predicted labels y_pred to calculate accuracy.

2fill in blank

medium

Complete the code to split the dataset into training and testing sets.

Computer Vision

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=[1], random_state=42)

Drag options to blanks, or click blank then click option'

A0.25

B0.5

C0.1

D0.75

Attempts:

3 left

💡 Hint

Common Mistakes

Setting test_size too high, leaving too little training data.

Using an integer instead of a float for test_size.

The test_size parameter defines the proportion of the dataset to include in the test split. 0.25 means 25% test data and 75% training data.

3fill in blank

hard

Fix the error in the code to compute the confusion matrix correctly.

Computer Vision

cm = confusion_matrix(y_true, [1])

Drag options to blanks, or click blank then click option'

Ay_train

By_test

Cy_val

Dy_pred

Attempts:

3 left

💡 Hint

Common Mistakes

Using test labels instead of predicted labels.

Mixing training labels with true labels.

The confusion matrix compares true labels y_true with predicted labels y_pred. Using other variables causes incorrect results or errors.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each class to its precision score.

Computer Vision

precision_scores = {cls: precision_score(y_true, y_pred, labels=[cls], average=[1]) for cls in [2]

Drag options to blanks, or click blank then click option'

Amacro

Bmicro

Cweighted

Dclasses

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'micro' average which aggregates globally.

Using an undefined variable instead of class labels.

The average='macro' calculates precision for each class independently. The variable classes holds the list of class labels to iterate over.

5fill in blank

hard

Fill all three blanks to compute the F1 score with macro averaging and print the result.

Computer Vision

f1 = f1_score(y_true, [1], average=[2])
print('F1 Score:', [3])

Drag options to blanks, or click blank then click option'

Ay_pred

Bmicro

Cf1

Dmacro

Attempts:

3 left

💡 Hint

Common Mistakes

Using micro average which aggregates globally.

Printing the wrong variable.

The F1 score is computed using predicted labels y_pred and macro averaging to treat all classes equally. The variable f1 holds the score to print.

Practice

1. Why is it important to use a separate test set when evaluating a computer vision model?

easy

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

B. To make the training process faster

C. To increase the size of the training data

D. To reduce the number of model parameters

Solution

1. Step 1: Understand the purpose of a test set

   The test set is data the model has never seen before, used to check real-world performance.

2. Step 2: Compare test set role with other options

   Options B, C, and D do not relate to evaluation but to training or model design.

3. Final Answer:

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

4. Quick Check:

   Test set = unseen data check [OK]

Hint: Test set = new data to check model accuracy [OK]

Common Mistakes:

• Confusing test set with training set
• Thinking test set speeds up training
• Believing test set changes model size

2. Which of the following is the correct way to split data for model evaluation in Python using scikit-learn?

easy

A. split_train_test(data, 0.2)

B. train_test_split(data, test_size=0.2, random_state=42)

C. train_test(data, 0.2)

D. test_train_split(data, 0.2)

Solution

1. Step 1: Recall the correct function name in scikit-learn

   The function to split data is called train_test_split with parameters like test_size and random_state.

2. Step 2: Check the options for correct syntax

   Only train_test_split(data, test_size=0.2, random_state=42) uses the correct function name and parameters; others are invalid or do not exist.

3. Final Answer:

   train_test_split(data, test_size=0.2, random_state=42) -> Option B

4. Quick Check:

   Correct function = train_test_split [OK]

Hint: Remember scikit-learn function: train_test_split [OK]

Common Mistakes:

• Using wrong function names
• Missing required parameters
• Confusing order of train and test

3. Given the following code snippet, what will be the printed accuracy?

from sklearn.metrics import accuracy_score
true_labels = [1, 0, 1, 1, 0]
pred_labels = [1, 0, 0, 1, 0]
accuracy = accuracy_score(true_labels, pred_labels)
print("{:.2f}".format(round(accuracy, 2)))

medium

A. 0.60

B. 0.40

C. 0.80

D. 1.00

Solution

1. Step 1: Compare true and predicted labels

   True: [1, 0, 1, 1, 0], Predicted: [1, 0, 0, 1, 0]. Matches at positions 0,1,3,4 (4 correct out of 5).

2. Step 2: Calculate accuracy

   Accuracy = correct predictions / total = 4/5 = 0.8. Rounded to 2 decimals is 0.80.

3. Final Answer:

   0.80 -> Option C

4. Quick Check:

   Accuracy = 4/5 = 0.80 [OK]

Hint: Count matches, divide by total labels [OK]

Common Mistakes:

• Counting wrong matches
• Not rounding accuracy
• Confusing accuracy with precision

4. You trained a model but the test accuracy is much higher than the training accuracy. What is the most likely issue?

medium

A. Data leakage between training and test sets

B. Model is underfitting the training data

C. Test set is too small

D. Training data is too large

Solution

1. Step 1: Understand unusual accuracy pattern

   Test accuracy higher than training is unusual and often means test data was seen during training.

2. Step 2: Identify cause from options

   Data leakage means test data accidentally used in training, causing inflated test accuracy.

3. Final Answer:

   Data leakage between training and test sets -> Option A

4. Quick Check:

   High test accuracy > training = data leakage [OK]

Hint: High test accuracy than train? Check data leakage [OK]

Common Mistakes:

• Assuming underfitting causes higher test accuracy
• Ignoring data leakage possibility
• Blaming test set size without evidence

5. You want to evaluate a computer vision model for detecting rare objects in images. Which evaluation metric is best to use and why?

hard

A. Confusion matrix, because it shows training time

B. Accuracy, because it shows overall correct predictions

C. Mean Squared Error, because it measures prediction error

D. F1 score, because it balances precision and recall for imbalanced data

Solution

1. Step 1: Understand the problem of rare object detection

   Rare objects mean data is imbalanced; many negatives, few positives.

2. Step 2: Choose metric suitable for imbalanced data

   F1 score balances precision (correct positive predictions) and recall (finding all positives), ideal for rare classes.

3. Final Answer:

   F1 score, because it balances precision and recall for imbalanced data -> Option D

4. Quick Check:

   Rare class? Use F1 score [OK]

Hint: Rare classes? Use F1 score for balance [OK]

Common Mistakes:

• Using accuracy which hides imbalance
• Confusing regression metrics with classification
• Misunderstanding confusion matrix purpose