Machine Learning Classification Evaluation Metrics

Area Under CurveAUC is one of the most widely used metrics for evaluation. 9 hours agoI am interested in finding metrics that assess the explanatory power of machine learning models involving binary classification.

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There are several evaluation metrics eg accuracy AUC-ROC Mathew correlation coefficient precision recall F1 score confusion matrix etc that are used to determine the performance of supervised machine learning classification algorithms.

Machine learning classification evaluation metrics. Before diving into the evaluation metrics for classification it is important to understand the confusion matrix. I understand that with Neural Networks it is difficult to determine anything about explanatory power. Classification evaluation metrics score generally indicates how correct we are about our prediction.

In classification problems we use two types of algorithms dependent on the kind of output it creates. Classification Evaluation Metrics. The evaluation metrics used in each of these models are different.

The higher the score the better our model is. Another measurement for your machine learning models would be loss functions. Being Humans we want to know the efficiency or the performance of any machine or software we come across.

For example if we consider a car we want to know the Mileage or if we there is a certain algorithm we want to know about the Time and Space Complexity similarly there must be some or the other way we can measure the efficiency or. It is used for binary classification problem. It is used for binary classification problem.

AUC of a classifier is equal to the probability that the classifier will rank a randomly chosen positive example higher than a randomly chosen negative example. Why do we need Evaluation Metrics. I know that for logistic regression McFadden pseudo R squared AIC and deviance can be used.

Generally speaking loss functions represent the price paid for inaccuracy of predictions.

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