Last Updated on August 21, 2020 The XGBoost algorithm is effective for a wide range of regression and classification predictive modeling problems. It is an efficient implementation of the stochastic gradient boosting algorithm and offers a range of hyperparameters that give fine-grained control over the model training procedure. Although the algorithm performs well in general, even on imbalanced classification datasets, it offers a way to tune the training algorithm to pay more attention to misclassification of the minority class for […]

Most machine learning algorithms assume that all misclassification errors made by a model are equal. This is often not the case for imbalanced classification problems where missing a positive or minority class case is worse than incorrectly classifying an example from the negative or majority class. There are many real-world examples, such as detecting spam email, diagnosing a medical condition, or identifying fraud. In all of these cases, a false negative (missing a case) is worse or more costly than […]

Last Updated on August 28, 2020 Classification predictive modeling typically involves predicting a class label. Nevertheless, many machine learning algorithms are capable of predicting a probability or scoring of class membership, and this must be interpreted before it can be mapped to a crisp class label. This is achieved by using a threshold, such as 0.5, where all values equal or greater than the threshold are mapped to one class and all other values are mapped to another class. For […]

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 […]