Classification reports in TensorFlow - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of classification report with imbalanced classes

Given a binary classification with true labels and predicted labels as below, what is the precision for class 1 in the classification report?

TensorFlow

from sklearn.metrics import classification_report

y_true = [0, 0, 0, 1, 1, 1, 1]
y_pred = [0, 0, 1, 1, 0, 1, 1]

report = classification_report(y_true, y_pred, output_dict=True)
precision_class_1 = report['1']['precision']
print(round(precision_class_1, 2))

A 0.60

B 0.67

C 0.80

D 0.75

💡 Hint

Precision is the ratio of true positives to all predicted positives for that class.

Precision for class 1 is calculated as TP / (TP + FP). Here, TP=3 (correctly predicted 1s), FP=1 (predicted 1 but true label 0), so precision = 3/(3+1) = 0.75. The code prints round(0.75, 2) = 0.75 (option D).

❓ Model Choice

intermediate

Choosing the right metric for multi-class classification

You have a multi-class classification problem with 5 classes and want to evaluate your TensorFlow model. Which metric from the classification report best shows the balance between precision and recall for each class?

A Accuracy

B F1-score

C Precision

D Recall

💡 Hint

Think about a metric that combines precision and recall.

F1-score is the harmonic mean of precision and recall, showing a balance between the two. It is especially useful when you want to consider both false positives and false negatives.

❓ Hyperparameter

advanced

Effect of threshold on classification report metrics

In a binary classification TensorFlow model, you adjust the decision threshold from 0.5 to 0.7. How does this change most likely affect the precision and recall for the positive class in the classification report?

A Precision increases, recall decreases

B Precision decreases, recall increases

C Both precision and recall increase

D Both precision and recall decrease

💡 Hint

Raising the threshold makes the model more strict about positive predictions.

Increasing the threshold means fewer positive predictions, so false positives reduce, increasing precision. But some true positives are missed, lowering recall.

🔧 Debug

advanced

Debugging unexpected classification report output

You run classification_report on your TensorFlow model predictions but see all zeros for precision, recall, and f1-score for one class. What is the most likely cause?

TensorFlow

from sklearn.metrics import classification_report

true_labels = [0, 1, 2, 2, 1]
pred_labels = [0, 0, 2, 2, 0]

print(classification_report(true_labels, pred_labels))

A The model never predicted that class, causing zero division in metrics

B The true labels are missing that class, so metrics are zero

C The classification_report function is not compatible with TensorFlow

D The labels are not integers, causing metric calculation errors

💡 Hint

Check if the model predicted any samples for that class.

If the model never predicts a class, precision and recall for that class become zero because there are no true positives or predicted positives, leading to zero metrics.

🧠 Conceptual

expert

Interpreting macro vs weighted averages in classification reports

In a multi-class classification report, what is the key difference between macro average and weighted average for precision, recall, and F1-score?

A Both macro and weighted averages weight metrics by class frequency

B Macro average weights metrics by class support; weighted average averages equally

C Macro average calculates metrics per class and averages equally; weighted average weights by class support

D Macro average only considers the largest class; weighted average considers all classes

💡 Hint

Think about how class size affects the averaging.

Macro average treats all classes equally regardless of size, while weighted average accounts for the number of true instances per class, giving more influence to larger classes.

Practice

1. What does a classification report in TensorFlow typically show?

easy

A. Precision, recall, and F1-score for each class

B. The model's training time

C. The number of layers in the model

D. The size of the input data

Solution

1. Step 1: Understand classification report contents

   A classification report summarizes key metrics like precision, recall, and F1-score for each class predicted by the model.

2. Step 2: Identify what is NOT included

   Training time, model layers, and input size are not part of the classification report.

3. Final Answer:

   Precision, recall, and F1-score for each class -> Option A

4. Quick Check:

   Classification report = precision, recall, F1-score [OK]

Hint: Look for metrics per class, not model structure or time [OK]

Common Mistakes:

• Confusing training time with performance metrics
• Thinking model architecture details are in the report
• Assuming input data size is shown

2. Which code snippet correctly imports the function to generate a classification report in TensorFlow's ecosystem?

easy

A. import classification_report from tensorflow

B. import tensorflow.classification_report as cr

C. from sklearn.metrics import classification_report

D. from tensorflow.metrics import classification_report

Solution

1. Step 1: Recall the source of classification_report

   The classification_report function is part of scikit-learn, not TensorFlow directly.

2. Step 2: Check import syntax correctness

   from sklearn.metrics import classification_report uses correct Python import syntax from sklearn.metrics. Other options are invalid or incorrect.

3. Final Answer:

   from sklearn.metrics import classification_report -> Option C

4. Quick Check:

   Import classification_report from sklearn.metrics [OK]

Hint: classification_report is from sklearn, not tensorflow [OK]

Common Mistakes:

• Trying to import classification_report directly from tensorflow
• Using incorrect import syntax
• Confusing TensorFlow metrics with sklearn metrics

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

from sklearn.metrics import classification_report
true_labels = [0, 1, 0, 1]
pred_labels = [0, 0, 0, 1]
print(classification_report(true_labels, pred_labels))

medium

A. Empty output with no metrics

B. A report showing precision, recall, and F1-score for classes 0 and 1

C. An error because labels are integers

D. Only accuracy score printed

Solution

1. Step 1: Understand input labels and predictions

   Labels are valid integers representing two classes: 0 and 1. Predictions have one mistake at index 1.

2. Step 2: What classification_report prints

   The function prints precision, recall, F1-score, and support for each class, not just accuracy or errors.

3. Final Answer:

   A report showing precision, recall, and F1-score for classes 0 and 1 -> Option B

4. Quick Check:

   classification_report outputs metrics per class [OK]

Hint: classification_report prints detailed metrics, not just accuracy [OK]

Common Mistakes:

• Expecting an error due to integer labels
• Thinking only accuracy is printed
• Assuming no output if predictions differ

4. Identify the error in this code snippet that tries to print a classification report:

from sklearn.metrics import classification_report
true = [1, 0, 1]
pred = [1, 0]
print(classification_report(true, pred))

medium

A. print statement syntax is wrong

B. classification_report is not imported correctly

C. Labels must be strings, not integers

D. The true and pred lists have different lengths

Solution

1. Step 1: Check input list lengths

   True labels list has 3 elements, predicted labels list has 2 elements. They must be the same length.

2. Step 2: Verify other code parts

   Import and print syntax are correct. Labels can be integers.

3. Final Answer:

   The true and pred lists have different lengths -> Option D

4. Quick Check:

   Input lists must match in length [OK]

Hint: Check if true and predicted label lists have same length [OK]

Common Mistakes:

• Ignoring length mismatch
• Thinking labels must be strings
• Assuming import or print is wrong

5. You trained a TensorFlow model for 3 classes: cat, dog, and rabbit. After predictions, you get this classification report:

              precision    recall  f1-score   support

         cat       0.90      0.95      0.92       100
         dog       0.70      0.60      0.65       100
      rabbit       0.50      0.40      0.44       100

    accuracy                           0.68       300
   macro avg       0.70      0.65      0.67       300
weighted avg       0.70      0.68      0.68       300

What should you focus on to improve your model?

hard

A. Improve the model's performance on the rabbit class

B. Increase the support for the cat class

C. Reduce the number of classes

D. Ignore the dog class since it has moderate scores

Solution

1. Step 1: Analyze class-wise metrics

   Rabbit class has the lowest precision (0.50), recall (0.40), and F1-score (0.44), indicating poor performance.

2. Step 2: Decide improvement focus

   Improving rabbit predictions will raise overall accuracy and balance. Increasing support or ignoring classes is not helpful.

3. Final Answer:

   Improve the model's performance on the rabbit class -> Option A

4. Quick Check:

   Lowest metrics class needs improvement [OK]

Hint: Focus on class with lowest F1-score to improve model [OK]

Common Mistakes:

• Trying to increase support instead of improving metrics
• Ignoring poorly performing classes
• Reducing classes without reason