Last Updated on August 21, 2020 The decision tree algorithm is effective for balanced classification, although it does not perform well on imbalanced datasets. The split points of the tree are chosen to best separate examples into two groups with minimum mixing. When both groups are dominated by examples from one class, the criterion used to select a split point will see good separation, when in fact, the examples from the minority class are being ignored. This problem can be […]

Last Updated on August 21, 2020 The Support Vector Machine algorithm is effective for balanced classification, although it does not perform well on imbalanced datasets. The SVM algorithm finds a hyperplane decision boundary that best splits the examples into two classes. The split is made soft through the use of a margin that allows some points to be misclassified. By default, this margin favors the majority class on imbalanced datasets, although it can be updated to take the importance of […]

Last Updated on August 21, 2020 Deep learning neural networks are a flexible class of machine learning algorithms that perform well on a wide range of problems. Neural networks are trained using the backpropagation of error algorithm that involves calculating errors made by the model on the training dataset and updating the model weights in proportion to those errors. The limitation of this method of training is that examples from each class are treated the same, which for imbalanced datasets […]

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 […]