![]() ![]() ![]() Based on those numbers, you can calculate some values that explain the performance of your model. In the case of a binary classifier, this would be the amount of true/false positive/negative. ![]() A confusion matrix plots the amount of amount of correct predictions against the amount of incorrect predictions. The training dataset is used to train your model, while the test dataset is used to measure the performance of your model.Ī commonly used method to measure the performance of a classification algorithm is a confusion matrix. Typically, you split a dataset into a training dataset and a test dataset. When building a machine learning model, it’s important to measure the results of your model. ![]()
0 Comments
Leave a Reply. |