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The 6 Metrics You Need to Optimize for Performance in Machine Learning - Exxact

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There are many metrics to measure the performance of your model depending on the type of machine learning you are looking to conduct. In this article, we take a look at performance measures for classification and regression models and discuss which is better optimized. Sometimes the metric to look at will vary according to the problem that is initially being solved. The True Positive Rate also called Recall is the go-to performance measure in binary/non-binary classification problems. Most if not all the time, we are only interested in correctly predicting one class.


6 Metrics You Need to Optimize for Performance in Machine Learning - DZone AI

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There are many metrics to measure the performance of your machine learning model depending on the type of machine learning you are looking to conduct. In this article, we take a look at performance measures for classification and regression models and discuss which is better-optimized. Sometimes the metric to look at will vary according to the problem that is initially being solved. The True Positive Rate, also called Recall, is the go-to performance measure in binary/non-binary classification problems. Most of the time -- if not all of the time -- we are only interested in correctly predicting one class.


The Basics: evaluating classifiers

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Judging a classification model feels like it should be an easier task than judging a regression. After all, your prediction from a classification model can only either be right or wrong, while a prediction from a regression model can be more or less wrong, can have any level of error, high or low. Yet, judging a classification is not as simple as it may seem. There's more than one way for a classification to be right or to be wrong, and multiple ways to combine the different ways to be right and wrong into a unified metric. Of course, all these different metrics have different, frequently unintuitive names -- precision, recall, F1, ROC curves -- making the process seem a little forbidding from the outside.


How to Calculate Precision, Recall, and F-Measure for Imbalanced Classification

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Classification accuracy is the total number of correct predictions divided by the total number of predictions made for a dataset. As a performance measure, accuracy is inappropriate for imbalanced classification problems. The main reason is that the overwhelming number of examples from the majority class (or classes) will overwhelm the number of examples in the minority class, meaning that even unskillful models can achieve accuracy scores of 90 percent, or 99 percent, depending on how severe the class imbalance happens to be. An alternative to using classification accuracy is to use precision and recall metrics. In this tutorial, you will discover how to calculate and develop an intuition for precision and recall for imbalanced classification.


7 Things You Should Know about ROC AUC

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Models for different classification problems can be fitted by trying to maximize or minimize various performance measures. Measurements that address one aspect of a model's performance but not another are important to note so that we can make an informed decision and select the performance measures that best fit our design. ROC AUC is commonly used in many fields as a prominent measure to evaluate classifier performance, and researchers might favor one classifier over another due to a higher AUC. For a refresher on ROC AUC, a clear and concise explanation can be found here. If you are totally unfamiliar with ROC AUC you may find that this post digs into the subject a bit too deep, but I hope you will still find it useful or bookmark it for future reference.