There are a number of ways to measure the performance of a machine learning model. The most common metric is accuracy, which measures the percentage of correctly classified instances. Other popular metrics include precision, recall, and F1 score.
TNR (True Negative Rate) = ( True Negative Actual Negative)
The TNR or true negative rate is the ratio of true negatives to all actual negatives. It is a measure of how well a model does at correctly identifying the negative class. A high TNR indicates that the model is good at correctly identifying the negative class. SNR (Signal-to-Noise Ratio) = (Predicted Positive/ Actual Positive): The SNR or signal-to-noise ratio is the ratio of predicted positives to all actual positives. It is a measure of how well a model does at correctly identifying the positive class. A high SNR indicates that the model is good at correctly identifying the positive class. FPR (False Positive Rate) = (Predicted Positive/ Actual Negative): The FPR or false positive rate is the ratio of predicted positives to all actual negatives. It measures how often the model incorrectly predicts an instance as belonging to the positive class when it actually belongs to the negative class. A low FPR indicates that the model does a good job at not predicting instances as belonging to the positive class when they actually belong to the negative class..
FPR (False Positive Rate) = ( False Positive Actual Negative )
The FPR tells us how often the positive class is incorrectly predicted as the negative class. A low FPR means that most of the positive examples are correctly recognized (a good thing), while a high FPR means that most of the positive examples are misclassified (a bad thing). TPR (True Positive Rate) = ( True Positive / Actual Positive ): The TPR tells us how often the positive class is correctly predicted as the positive class. A high TPR means that most of the positive examples are correctly recognized (a good thing), while a low TPR means that most of the positive examples are misclassified (a bad thing).
F1 Score: The F1 score is a measure of a model’s accuracy. It takes into account both false positives and false negatives, and assigns a score based on how well the model performs. A high F1 score indicates a model that is very accurate, while a low F1 score indicates a model that is not very accurate.
FNR (False Negative Rate) = ( False Negative Actual Positive )
FNR is a measure of the performance of a model in machine learning. It is the ratio of false negatives to actual positives. A high FNR indicates that the model is not performing well, as it is not correctly identifying positive examples.
“You can’t improve what you can’t measure.” -Peter Drucker
Related Articles:
