Classification reports in TensorFlow - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the function that generates a classification report.

TensorFlow

from sklearn.metrics import [1]

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

Aclassification_report

Baccuracy_score

Cconfusion_matrix

Droc_auc_score

Attempts:

3 left

💡 Hint

Common Mistakes

Importing confusion_matrix instead of classification_report

Using accuracy_score which only gives one metric

The classification_report function from sklearn.metrics provides detailed metrics for classification models.

2fill in blank

medium

Complete the code to generate predictions from the model on test data.

TensorFlow

y_pred = model.[1](X_test)

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

Aevaluate

Bpredict

Cfit

Dtransform

Attempts:

3 left

💡 Hint

Common Mistakes

Using evaluate which returns loss and metrics

Using fit which trains the model

The predict method generates predicted labels or probabilities from the model.

3fill in blank

hard

Fix the error in the code to correctly generate a classification report for true and predicted labels.

TensorFlow

report = classification_report(y_true, [1])
print(report)

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

Ay_pred

Bmodel.predict(X_test)

Cy_true

DX_test

Attempts:

3 left

💡 Hint

Common Mistakes

Passing y_true twice

Passing input features X_test instead of predictions

The classification report requires the true labels and predicted labels as inputs. Here, y_pred holds the predicted labels.

4fill in blank

hard

Fill both blanks to create a classification report with target names for classes 'cat' and 'dog'.

TensorFlow

report = classification_report(y_true, y_pred, target_names=[1])
print([2])

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

A['cat', 'dog']

Bclassification_report

Creport

D['dog', 'cat']

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the function name instead of the report variable to print

Swapping class names order incorrectly

The target_names parameter expects a list of class names. The variable report holds the generated report to print.

5fill in blank

hard

Fill all three blanks to generate predictions, convert probabilities to class labels, and print the classification report.

TensorFlow

y_prob = model.[1](X_test)
y_pred = y_prob.argmax(axis=[2])
print(classification_report(y_true, [3]))

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

Apredict

B1

Cy_pred

Dpredict_proba

Attempts:

3 left

💡 Hint

Common Mistakes

Using predict instead of predict_proba for probabilities

Using wrong axis in argmax

Printing classification_report with wrong variable

Use predict_proba to get probabilities, then use argmax along axis 1 to get class labels, and finally print the report with predicted labels.

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