Time series evaluation metrics in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the Mean Absolute Error (MAE) between true and predicted values.

ML Python

from sklearn.metrics import [1]
true = [3, -0.5, 2, 7]
pred = [2.5, 0.0, 2, 8]
mae = [1](true, pred)
print(mae)

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

Ar2_score

Bmean_absolute_error

Caccuracy_score

Dmean_squared_error

Attempts:

3 left

💡 Hint

Common Mistakes

Using mean_squared_error instead of mean_absolute_error

Using accuracy_score which is for classification

The Mean Absolute Error (MAE) measures the average absolute difference between true and predicted values. The function mean_absolute_error from sklearn.metrics computes this metric.

2fill in blank

medium

Complete the code to calculate the Root Mean Squared Error (RMSE) from the Mean Squared Error (MSE).

ML Python

from sklearn.metrics import mean_squared_error
import numpy as np
true = [3, -0.5, 2, 7]
pred = [2.5, 0.0, 2, 8]
mse = mean_squared_error(true, pred)
rmse = np.[1](mse)
print(rmse)

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

Asquare

Bexp

Clog

Dsqrt

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.square instead of np.sqrt

Using np.log or np.exp which are unrelated

The Root Mean Squared Error (RMSE) is the square root of the Mean Squared Error (MSE). The np.sqrt function calculates the square root.

3fill in blank

hard

Fix the error in the code to calculate Mean Absolute Percentage Error (MAPE) manually.

ML Python

true = [100, 200, 300, 400]
pred = [110, 190, 310, 420]
mape = 100 * sum(abs((true[i] - pred[i]) / [1]) for i in range(len(true))) / len(true)
print(mape)

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

Asum(true)

Bpred[i]

Ctrue[i]

Dlen(true)

Attempts:

3 left

💡 Hint

Common Mistakes

Dividing by predicted value instead of true value

Dividing by sum or length instead of element-wise true value

Mean Absolute Percentage Error (MAPE) divides the absolute error by the true value at each point. So the denominator should be true[i].

4fill in blank

hard

Fill both blanks to create a dictionary of errors where keys are error names and values are their computed scores.

ML Python

from sklearn.metrics import mean_absolute_error, mean_squared_error
true = [1, 2, 3, 4]
pred = [1.1, 1.9, 3.2, 3.8]
errors = {
    'MAE': [1](true, pred),
    'MSE': [2](true, pred)
}
print(errors)

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

Amean_absolute_error

Bmean_squared_error

Cr2_score

Daccuracy_score

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up MAE and MSE function names

Using classification metrics like accuracy_score

The dictionary stores the Mean Absolute Error (MAE) and Mean Squared Error (MSE) calculated using their respective functions.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps each error name to its score, filtering only errors with score less than 0.5.

ML Python

from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
true = [2, 4, 6, 8]
pred = [2.1, 3.9, 6.2, 7.8]
errors = {
    [1]: [2](true, pred)
    for [3] in ['mean_absolute_error', 'mean_squared_error', 'r2_score']
    if [2](true, pred) < 0.5
}
print(errors)

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

Aerror_name

Bglobals()[error_name]

Derror

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names inconsistently

Trying to call functions without using globals() or similar

The dictionary comprehension uses error_name as key, calls the function by name using globals()[error_name], and iterates over error_name in the list of metric names. It filters errors with score less than 0.5.