How to calculate accuracy in python

Accuracy is a common metric used in machine learning and data analysis to evaluate the performance of classification models. It measures how many predictions made by a model are correct out of the total number of predictions and is typically expressed as a percentage.

Python Code to Calculate Accuracy

import numpy as np
from sklearn.metrics import accuracy_score

# Ground truth labels
true_labels = [1, 0, 1, 1, 0, 1, 0, 1]

# Predicted labels from your model
predicted_labels = [1, 0, 1, 1, 1, 1, 0, 0]

# Calculate accuracy
accuracy = accuracy_score(true_labels, predicted_labels)
accuracy_percentage = accuracy * 100

print(f"Accuracy: {accuracy_percentage:.2f}%")

Here’s how the code works:

  1. Import the necessary libraries, including NumPy and scikit-learn.
  2. Prepare your data, which includes the ground truth labels and predicted labels.
  3. Calculate accuracy using the accuracy_score function from scikit-learn.
  4. Print or use the accuracy value as needed.
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The calculated accuracy for the given data is 75.00%.

Accuracy is an essential metric for assessing the performance of your machine learning models and is used to gauge how well your model’s predictions match the actual data.