Archive | Imbalanced Classification

A Gentle Introduction to Probability Metrics for Imbalanced Classification

A Gentle Introduction to Probability Metrics for Imbalanced Classification

Classification predictive modeling involves predicting a class label for examples, although some problems require the prediction of a probability of class membership. For these problems, the crisp class labels are not required, and instead, the likelihood that each example belonging to each class is required and later interpreted. As such, small relative probabilities can carry […]

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Precision-Recall Curve of a Logistic Regression Model and a No Skill Classifier

ROC Curves and Precision-Recall Curves for Imbalanced Classification

Most imbalanced classification problems involve two classes: a negative case with the majority of examples and a positive case with a minority of examples. Two diagnostic tools that help in the interpretation of binary (two-class) classification predictive models are ROC Curves and Precision-Recall curves. Plots from the curves can be created and used to understand […]

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

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

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 […]

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Scatter Plot of Binary Classification Dataset With 1 to 100 Class Distribution

Failure of Classification Accuracy for Imbalanced Class Distributions

Classification accuracy is a metric that summarizes the performance of a classification model as the number of correct predictions divided by the total number of predictions. It is easy to calculate and intuitive to understand, making it the most common metric used for evaluating classifier models. This intuition breaks down when the distribution of examples […]

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Standard Machine Learning Datasets for Imbalanced Classification

Standard Machine Learning Datasets for Imbalanced Classification

An imbalanced classification problem is a problem that involves predicting a class label where the distribution of class labels in the training dataset is skewed. Many real-world classification problems have an imbalanced class distribution, therefore it is important for machine learning practitioners to get familiar with working with these types of problems. In this tutorial, […]

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Scatter Plot of Binary Classification Dataset With Provided Class Distribution

Develop an Intuition for Severely Skewed Class Distributions

An imbalanced classification problem is a problem that involves predicting a class label where the distribution of class labels in the training dataset is not equal. A challenge for beginners working with imbalanced classification problems is what a specific skewed class distribution means. For example, what is the difference and implication for a 1:10 vs. […]

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A Gentle Introduction to Imbalanced Classification

A Gentle Introduction to Imbalanced Classification

Classification predictive modeling involves predicting a class label for a given observation. An imbalanced classification problem is an example of a classification problem where the distribution of examples across the known classes is biased or skewed. The distribution can vary from a slight bias to a severe imbalance where there is one example in the […]

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