Скачать или смотреть Confusion Matrix in Data Science | Data Analysis | Machine learning

confusion matrix is a matrix that summarizes the performance of a machine learning model on a set of test data. It is often used to measure the performance of classification models, which aim to predict a categorical label for each input instance. The matrix displays the number of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) produced by the model on the test data.


True Positive (TP): It is the total counts having both predicted and actual values are Dog.

True Negative (TN): It is the total counts having both predicted and actual values are Not Dog.

False Positive (FP): It is the total counts having prediction is Dog while actually Not Dog.

False Negative (FN): It is the total counts having prediction is Not Dog while actually, it is Dog.

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