Classification models are widely used in machine learning to classify data into different categories. One of the most commonly used metrics to evaluate the performance of a classification model is accuracy. However, accuracy alone may not provide a complete picture of the model’s performance. There are several other metrics that can be used to evaluate the classification models, such as precision, recall, F1-score, and AUC-ROC.
Precision measures the proportion of true positives among all the positive predictions made by the model, while recall measures the proportion of true positives among all the actual positive instances in the data. F1-score is a harmonic mean of precision and recall and is a good metric to use when the dataset is imbalanced. AUC-ROC is a metric that measures the area under the receiver operating characteristic curve and is useful when the model outputs a probability score for each instance.
Why Accuracy is Not Enough
When evaluating classification models, accuracy is often the first metric that comes to mind. However, relying solely on accuracy can lead to misleading results and poor decision-making. In this section, we will explore why accuracy is not enough and the limitations of this metric.
Types of Classification Errors
Accuracy measures the proportion of correct predictions over the total number of predictions. However, it does not take into account the types of errors made by the model. There are two types of classification errors: false positives and false negatives.
False positives occur when the model predicts a positive outcome when the actual outcome is negative. False negatives occur when the model predicts a negative outcome when the actual outcome is positive. Both types of errors have different implications depending on the context of the classification problem.
For example, in medical diagnosis, a false negative can be more dangerous than a false positive. If a patient is diagnosed with a disease when they do not have it, they may undergo unnecessary treatment. However, if a patient is not diagnosed with a disease when they have it, they may not receive necessary treatment, which can be life-threatening.
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