Last Updated on August 21, 2020 Bagging is an ensemble algorithm that fits multiple models on different subsets of a training dataset, then combines the predictions from all models. Random forest is an extension of bagging that also randomly selects subsets of features used in each data sample. Both bagging and random forests have proven effective on a wide range of different predictive modeling problems. Although effective, they are not suited to classification problems with a skewed class distribution. Nevertheless, […]

Last Updated on August 21, 2020 Outliers or anomalies are rare examples that do not fit in with the rest of the data. Identifying outliers in data is referred to as outlier or anomaly detection and a subfield of machine learning focused on this problem is referred to as one-class classification. These are unsupervised learning algorithms that attempt to model “normal” examples in order to classify new examples as either normal or abnormal (e.g. outliers). One-class classification algorithms can be […]

Imbalanced classification is primarily challenging as a predictive modeling task because of the severely skewed class distribution. This is the cause for poor performance with traditional machine learning models and evaluation metrics that assume a balanced class distribution. Nevertheless, there are additional properties of a classification dataset that are not only challenging for predictive modeling but also increase or compound the difficulty when modeling imbalanced datasets. In this tutorial, you will discover data characteristics that compound the challenge of imbalanced […]

Last Updated on August 21, 2020 Developing a probabilistic model is challenging in general, although it is made more so when there is skew in the distribution of cases, referred to as an imbalanced dataset. The Haberman Dataset describes the five year or greater survival of breast cancer patient patients in the 1950s and 1960s and mostly contains patients that survive. This standard machine learning dataset can be used as the basis of developing a probabilistic model that predicts the […]

Last Updated on August 21, 2020 Many imbalanced classification tasks require a skillful model that predicts a crisp class label, where both classes are equally important. An example of an imbalanced classification problem where a class label is required and both classes are equally important is the detection of oil spills or slicks in satellite images. The detection of a spill requires mobilizing an expensive response, and missing an event is equally expensive, causing damage to the environment. One way […]

Fbeta-measure is a configurable single-score metric for evaluating a binary classification model based on the predictions made for the positive class. The Fbeta-measure is calculated using precision and recall. Precision is a metric that calculates the percentage of correct predictions for the positive class. Recall calculates the percentage of correct predictions for the positive class out of all positive predictions that could be made. Maximizing precision will minimize the false-positive errors, whereas maximizing recall will minimize the false-negative errors. The […]

Last Updated on August 21, 2020 Many machine learning models are capable of predicting a probability or probability-like scores for class membership. Probabilities provide a required level of granularity for evaluating and comparing models, especially on imbalanced classification problems where tools like ROC Curves are used to interpret predictions and the ROC AUC metric is used to compare model performance, both of which use probabilities. Unfortunately, the probabilities or probability-like scores predicted by many models are not calibrated. This means […]

Last Updated on August 28, 2020 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 […]

Last Updated on August 21, 2020 Cancer detection is a popular example of an imbalanced classification problem because there are often significantly more cases of non-cancer than actual cancer. A standard imbalanced classification dataset is the mammography dataset that involves detecting breast cancer from radiological scans, specifically the presence of clusters of microcalcifications that appear bright on a mammogram. This dataset was constructed by scanning the images, segmenting them into candidate objects, and using computer vision techniques to describe each […]

Last Updated on August 21, 2020 Many binary classification tasks do not have an equal number of examples from each class, e.g. the class distribution is skewed or imbalanced. Nevertheless, accuracy is equally important in both classes. An example is the classification of vowel sounds from European languages as either nasal or oral on speech recognition where there are many more examples of nasal than oral vowels. Classification accuracy is important for both classes, although accuracy as a metric cannot […]