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 […]
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 […]
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 […]
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 […]
Machine learning techniques often fail or give misleadingly optimistic performance on classification datasets with an imbalanced class distribution. The reason is that many machine learning algorithms are designed to operate on classification data with an equal number of observations for each class. When this is not the case, algorithms can learn that very few examples […]
Resampling methods are designed to change the composition of a training dataset for an imbalanced classification task. Most of the attention of resampling methods for imbalanced classification is put on oversampling the minority class. Nevertheless, a suite of techniques has been developed for undersampling the majority class that can be used in conjunction with effective […]
Imbalanced classification involves developing predictive models on classification datasets that have a severe class imbalance. The challenge of working with imbalanced datasets is that most machine learning techniques will ignore, and in turn have poor performance on, the minority class, although typically it is performance on the minority class that is most important. One approach […]
Imbalanced Classification Crash Course. Get on top of imbalanced classification in 7 days. Classification predictive modeling is the task of assigning a label to an example. Imbalanced classification are those classification tasks where the distribution of examples across the classes is not equal. Practical imbalanced classification requires the use of a suite of specialized techniques, […]
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 […]
The behavior and performance of many machine learning algorithms are referred to as stochastic. Stochastic refers to a variable process where the outcome involves some randomness and has some uncertainty. It is a mathematical term and is closely related to “randomness” and “probabilistic” and can be contrasted to the idea of “deterministic.” The stochastic nature […]
