Confusion Matrix is the performance measurement for machine learning classification problem where output can be 2 or more classes.
There are a number of different performance measures that can be used to evaluate the performance of a classification model on a multiclass classification problem, where the output can take on two or more classes, they all together are called confusion matrix. Some common measures include:
Accuracy: This is the most common evaluation metric for classification problems. It is the proportion of correct predictions made by the model, with respect to the total number of predictions.
Precision: This is the proportion of true positive predictions made by the model, with respect to all positive predictions made by the model.
Recall: This is the proportion of true positive predictions made by the model, with respect to all actual positive instances in the dataset.
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