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Tour of Evaluation Metrics for Imbalanced Classification

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A classifier is only as good as the metric used to evaluate it. If you choose the wrong metric to evaluate your models, you are likely to choose a poor model, or in the worst case, be misled about the expected performance of your model. Choosing an appropriate metric is challenging generally in applied machine learning, but is particularly difficult for imbalanced classification problems. Firstly, because most of the standard metrics that are widely used assume a balanced class distribution, and because typically not all classes, and therefore, not all prediction errors, are equal for imbalanced classification. In this tutorial, you will discover metrics that you can use for imbalanced classification. Tour of Evaluation Metrics for Imbalanced Classification Photo by Travis Wise, some rights reserved.


Develop a Model for the Imbalanced Classification of Good and Bad Credit

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Misclassification errors on the minority class are more important than other types of prediction errors for some imbalanced classification tasks. One example is the problem of classifying bank customers as to whether they should receive a loan or not. Giving a loan to a bad customer marked as a good customer results in a greater cost to the bank than denying a loan to a good customer marked as a bad customer. This requires careful selection of a performance metric that both promotes minimizing misclassification errors in general, and favors minimizing one type of misclassification error over another. The German credit dataset is a standard imbalanced classification dataset that has this property of differing costs to misclassification errors. Models evaluated on this dataset can be evaluated using the Fbeta-Measure that provides a way of both quantifying model performance generally, and captures the requirement that one type of misclassification error is more costly than another. In this tutorial, you will discover how to develop and evaluate a model for the imbalanced German credit classification dataset. Develop an Imbalanced Classification Model to Predict Good and Bad Credit Photo by AL Nieves, some rights reserved.


Develop a Model for the Imbalanced Classification of Good and Bad Credit

#artificialintelligence

Misclassification errors on the minority class are more important than other types of prediction errors for some imbalanced classification tasks. One example is the problem of classifying bank customers as to whether they should receive a loan or not. Giving a loan to a bad customer marked as a good customer results in a greater cost to the bank than denying a loan to a good customer marked as a bad customer. This requires careful selection of a performance metric that both promotes minimizing misclassification errors in general, and favors minimizing one type of misclassification error over another. The German credit dataset is a standard imbalanced classification dataset that has this property of differing costs to misclassification errors. Models evaluated on this dataset can be evaluated using the Fbeta-Measure that provides a way of both quantifying model performance generally, and captures the requirement that one type of misclassification error is more costly than another. In this tutorial, you will discover how to develop and evaluate a model for the imbalanced German credit classification dataset. Develop an Imbalanced Classification Model to Predict Good and Bad Credit Photo by AL Nieves, some rights reserved.


Develop a Model for the Imbalanced Classification of Good and Bad Credit - AnalyticsWeek

#artificialintelligence

Misclassification errors on the minority class are more important than other types of prediction errors for some imbalanced classification tasks. One example is the problem of classifying bank customers as to whether they should receive a loan or not. Giving a loan to a bad customer marked as a good customer results in a greater cost to the bank than denying a loan to a good customer marked as a bad customer. This requires careful selection of a performance metric that both promotes minimizing misclassification errors in general, and favors minimizing one type of misclassification error over another. The German credit dataset is a standard imbalanced classification dataset that has this property of differing costs to misclassification errors. Models evaluated on this dataset can be evaluated using the Fbeta-Measure that provides a way of both quantifying model performance generally, and captures the requirement that one type of misclassification error is more costly than another. In this tutorial, you will discover how to develop and evaluate a model for the imbalanced German credit classification dataset. Develop an Imbalanced Classification Model to Predict Good and Bad Credit Photo by AL Nieves, some rights reserved.


What Is the Naive Classifier for Each Imbalanced Classification Metric?

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A common mistake made by beginners is to apply machine learning algorithms to a problem without establishing a performance baseline. A performance baseline provides a minimum score above which a model is considered to have skill on the dataset. It also provides a point of relative improvement for all models evaluated on the dataset. A baseline can be established using a naive classifier, such as predicting one class label for all examples in the test dataset. Another common mistake made by beginners is using classification accuracy as a performance metric on problems that have an imbalanced class distribution.