accuracy_score: Classification Accuracy Metric

The accuracy_score function computes the accuracy of classification models by comparing predicted labels to true labels. It is a fundamental metric for evaluating the performance of classifiers in binary and multiclass settings.

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Overview

Accuracy is defined as the proportion of correct predictions among the total number of cases examined. It is a simple yet powerful metric for assessing how well a model is performing, especially when the classes are balanced.

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Parameters

Parameter	Type	Description
y_true	array-like or pandas Series	Ground truth (correct) target values. Shape: (n_samples,)
y_pred	array-like or pandas Series	Estimated target values as returned by a classifier. Shape: (n_samples,)

• Both y_true and y_pred can be numpy arrays, lists, or pandas Series.
• If a pandas DataFrame is passed, a ValueError is raised (select a column instead).

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Returns

• accuracy: float
  The accuracy score as a float in the range [0, 1].

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Raises

• ValueError
• If y_true or y_pred is a pandas DataFrame (must select a column).
• If the shapes of y_true and y_pred do not match.

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Example Usage

from machinegnostics.metrics import accuracy_score

# Example 1: Using lists
y_true = [0, 1, 2, 2, 0]
y_pred = [0, 0, 2, 2, 0]
print(accuracy_score(y_true, y_pred))  # Output: 0.8

# Example 2: Using pandas Series
import pandas as pd
df = pd.DataFrame({'true': [1, 0, 1], 'pred': [1, 1, 1]})
print(accuracy_score(df['true'], df['pred']))  # Output: 0.6666666666666666

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Notes

• The function supports input as numpy arrays, lists, or pandas Series.
• If you pass a pandas DataFrame, you must select a column (e.g., df['col']), not the whole DataFrame.
• The accuracy metric is most informative when the dataset is balanced. For imbalanced datasets, consider additional metrics such as precision, recall, or F1 score.

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