Sunday , November 10 2024

Classification

F1 Score

F1-Score The F1 Score is a metric used to evaluate the performance of a classification model by combining both precision and recall into a single score. It provides a balance between precision and recall, especially when there is an uneven class distribution or when both false positives and false negatives …

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Precision Score

Precision Score Precision Score is a metric used in classification tasks to measure how many of the positive predictions made by a model are actually correct. In simpler terms, it answers the question: Out of all the instances the model predicted as positive, how many were truly positive? The formula …

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Recall Score

Recall Score The Recall Score (also known as Sensitivity or True Positive Rate) measures the ability of a classification model to correctly identify all relevant (positive) instances. In other words, it answers the question: Out of all the actual positive cases, how many did the model correctly predict? The formula …

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Accuracy, Precision, Recall, and F1-Score

Accuracy, Precision, Recall, and F1-Score Accuracy, precision, recall, and F1-score are commonly used performance metrics to evaluate the effectiveness of a classification model. These metrics provide insights into different aspects of the model’s performance in predicting class labels. Here’s a brief explanation of each metric: 1. Accuracy: Accuracy measures the …

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