Python Coding challenge - Day 408| What is the output of the following Python Code?

 

1. Importing the Required Library

from sklearn.metrics import accuracy_score

sklearn.metrics: This is a module in the Scikit-Learn library that provides functions to evaluate the performance of machine learning models.

accuracy_score(): This function calculates the accuracy of a model's predictions.

It compares the predicted values to the actual values and computes the accuracy using the formula:

Accuracy=Number of Correct Predictions/Total Number of Predictions

2. Defining the Actual Values (True Labels)

y_true = [0, 1, 1, 0]

y_true represents the actual values or ground truth labels.

In this case, there are 4 data points labeled as 0 or 1.

0: Usually represents Negative (e.g., No disease, No spam, etc.)

1: Represents Positive (e.g., Disease present, Spam detected, etc.)

3. Defining the Predicted Values

y_pred = [0, 1, 0, 0]

y_pred contains the predictions made by a machine learning model for the same 4 data points.

Each value is either 0 or 1, indicating the predicted class.

4. Calculating the Accuracy Score

print(accuracy_score(y_true, y_pred))

The accuracy_score() function takes two inputs:

y_true → Actual values

y_pred → Predicted values

Final Output:

0.75