Evaluation metrics (RMSE, precision@k) in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the RMSE between true and predicted values.

ML Python

import numpy as np

def rmse(y_true, y_pred):
    return np.sqrt(np.mean((y_true - y_pred)[1]2))

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

A**

B*

C+

D-

Attempts:

3 left

💡 Hint

Common Mistakes

Using multiplication (*) instead of power (**) to square the difference.

Adding (+) or subtracting (-) instead of squaring.

The RMSE formula squares the difference between true and predicted values, so we use the power operator ** with 2.

2fill in blank

medium

Complete the code to calculate precision@k for a list of predicted scores and true labels.

ML Python

def precision_at_k(y_true, y_scores, k):
    top_k_indices = sorted(range(len(y_scores)), key=lambda i: y_scores[i], reverse=True)[:[1]]
    relevant = sum(y_true[i] for i in top_k_indices)
    return relevant / k

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Alen(y_scores)

Bsum(y_true)

Ck

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using the length of all scores instead of k.

Using zero or sum of true labels incorrectly.

Precision@k measures how many of the top k predicted items are relevant, so we slice the top k indices.

3fill in blank

hard

Fix the error in the RMSE calculation by completing the code.

ML Python

def rmse(y_true, y_pred):
    differences = y_true - y_pred
    squared_diff = differences[1]2
    mean_squared_diff = squared_diff.mean()
    return mean_squared_diff ** 0.5

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

A-

B*

C+

D**

Attempts:

3 left

💡 Hint

Common Mistakes

Using multiplication (*) instead of power (**).

Using addition or subtraction operators.

The differences must be squared using the power operator ** to correctly compute RMSE.

4fill in blank

hard

Fill both blanks to complete the precision@k calculation correctly.

ML Python

def precision_at_k(y_true, y_scores, k):
    top_k_indices = sorted(range(len(y_scores)), key=lambda i: y_scores[i], reverse=True)[:[1]]
    relevant = sum(y_true[i] for i in top_k_indices)
    return relevant [2] k

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

Ak

Blen(y_scores)

C/

D*

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong number for slicing top items.

Multiplying instead of dividing for precision.

We select the top k items and divide the count of relevant items by k to get precision@k.

5fill in blank

hard

Fill all three blanks to complete the RMSE function with numpy correctly.

ML Python

import numpy as np

def rmse(y_true, y_pred):
    error = y_true [1] y_pred
    squared_error = error [2] 2
    mean_squared_error = np.[3](squared_error)
    return np.sqrt(mean_squared_error)

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

A-

B**

Cmean

D+

Attempts:

3 left

💡 Hint

Common Mistakes

Adding instead of subtracting errors.

Using multiplication instead of power for squaring.

Using sum instead of mean for averaging.

RMSE is calculated by subtracting predictions from true values, squaring the errors, then taking the mean and square root.