Task: Compute the R-squared Value in Regression Analysis
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R-squared, also known as the coefficient of determination, is a measure that indicates how well the independent variables explain the variability of the dependent variable in a regression model.
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Your Task: To implement the function
r_squared(y_true, y_pred)that calculates the R-squared value, given arrays of true valuesy_trueand predicted valuesy_pred.
Examples
Example 1:
Input:
import numpy as np
y_true = np.array([1, 2, 3, 4, 5])
y_pred = np.array([1.1, 2.1, 2.9, 4.2, 4.8])
print(r_squared(y_true, y_pred))Output:
0.989Explanation: The R-squared value is calculated to be 0.989, indicating that the regression model explains 98.9% of the variance in the dependent variable.
Starter Code
import numpy as np
def r_squared(y_true, y_pred):
# Write your code here
pass
Python3
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