What is accuracy in machine learning?
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Accuracy in machine learning refers to how well a model can correctly predict outcomes. It is a measure of the percentage of correct predictions made by the model on a given dataset. Essentially, accuracy tells us how often the model’s predictions match the actual outcomes. For example, if a model has an accuracy of 85%, it means that it correctly predicts the outcome 85 out of 100 times.
It’s important to note that accuracy is a valuable metric, but it may not always be the best indicator of a model’s performance, especially when dealing with imbalanced datasets or specific business needs. In such cases, other metrics like precision, recall, or F1 score may provide a more nuanced evaluation of the model’s effectiveness.
Overall, accuracy is a key measure used to assess the performance of a machine learning model, but it’s essential to consider it in conjunction with other metrics for a comprehensive understanding of the model’s predictive capabilities.