Definition of Accuracy

Accuracy is the degree to which a model correctly predicts the value of a given data point.

How is Accuracy considered in relation to Machine Learning?

Accuracy is a metric used to evaluate the performance of a model on a given data set. It measures the proportion of correct predictions made by the model in comparison to the total number of samples it was applied to. Accuracy is one of the most common metrics for assessing the quality of machine learning models, as it provides an easy-to-understand and direct measure of how well a model can predict target values. Although accuracy can be an effective way to measure performance, it has its drawbacks; for instance, it does not take into account false positives or false negatives which may lead to incorrect conclusions about model performance. Additionally, accuracy is heavily dependent on the ratio between positive and negative class labels in training data sets and can thus be biased if one class dominates over another in terms of samples. To overcome these issues, other metrics such as precision, recall and F1 score are often used alongside accuracy in order to get a more comprehensive view on model performance.

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