Confusion matrix analysis in TensorFlow - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the function that computes a confusion matrix.

TensorFlow

from sklearn.metrics import [1]

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Aaccuracy_score

Bconfusion_matrix

Cclassification_report

Droc_auc_score

Attempts:

3 left

💡 Hint

Common Mistakes

Importing accuracy_score instead of confusion_matrix

Using classification_report which summarizes metrics but does not return the matrix

Trying to import from tensorflow instead of sklearn.metrics

The confusion_matrix function from sklearn.metrics computes the confusion matrix for classification results.

2fill in blank

medium

Complete the code to compute the confusion matrix from true and predicted labels.

TensorFlow

cm = confusion_matrix([1], y_pred)

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Apredictions

By_pred

Clabels

Dy_true

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping true and predicted labels

Using variable names that do not exist in the code

The first argument to confusion_matrix is the true labels, usually named y_true.

3fill in blank

hard

Fix the error in the code to display the confusion matrix as a heatmap using matplotlib.

TensorFlow

import matplotlib.pyplot as plt
import seaborn as sns
sns.heatmap([1], annot=True, fmt='d')
plt.xlabel('Predicted')
plt.ylabel('Actual')
plt.show()

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Acm

By_true

Cy_pred

Dconfusion_matrix

Attempts:

3 left

💡 Hint

Common Mistakes

Passing true or predicted labels instead of the matrix

Passing the function name instead of the matrix variable

The heatmap function needs the confusion matrix data, which is stored in the variable cm.

4fill in blank

hard

Fill both blanks to calculate precision and recall from the confusion matrix.

TensorFlow

precision = cm[[1], [2]] / cm[:, [2]].sum()

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A1

B0

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong indices for true positives

Summing over rows instead of columns

Precision is calculated as true positives (row 1, col 1) divided by all predicted positives (sum of column 1).

5fill in blank

hard

Fill all three blanks to calculate recall from the confusion matrix.

TensorFlow

recall = cm[[1], [2]] / cm[[3], :].sum()

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A1

B0

Attempts:

3 left

💡 Hint

Common Mistakes

Using column sums instead of row sums

Using wrong indices for true positives

Recall is true positives (row 1, col 1) divided by all actual positives (sum of row 1).