.The simple split partitions the data into two mutually exclusive subsets called a training set and a test set . It is common to designate two-thirds of the data as the training set and the remaining one-third as the test set.
How do you measure accuracy of classification models?
There are many ways for measuring classification performance. Accuracy, confusion matrix, log-loss, and AUC-ROC are some of the most popular metrics. Precision-recall is a widely used metrics for classification problems
Classification accuracy:
is a metric that summarizes the performance of a classification model as the number of correct predictions divided by the total number of predictions. It is easy to calculate and intuitive to understand, making it the most common metric used for evaluating classifier models.
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