Classification is the machine learning task of assigning a label to each data point in a dataset. The label can be either positive or negative, or it can be one of several classes. Classification problems can be categorized into two types, binary classification and multiclass or multilabel classification. In this article, we’ll focus on the latter.

Most people who are new to machine learning start with binary classification, where there are only two possible outcomes for each data point (for example spam or not spam or fraud or not fraud). However, many more complex applications of machine learning involve multiclass or multilabel classification, where one data point can belong to more than two classes. These can be solved using multiple methods, and we’ll discuss some of these strategies in this article. In multiclass classification, it is common to model a class label as a Multinoulli distribution where each output represents the probability that the data point belongs to a given class. This type of model is very effective and is commonly used with kernel algorithms like SVM, Logistic Regression, and Support Vector Machines.

The main challenge in multiclass classification is ensuring that the model can adequately distinguish between the different classes of the data. It can be challenging to do this because it requires the classifier to train on an exhaustively labelled training set. This can be difficult to do, especially for large and highly imbalanced datasets. Fortunately, a number of techniques have been developed that reduce the complexity of multiclass classification by reducing it to multiple binary classification problems. This can be done using many of the same techniques that are used in binary classification, including neural networks, decision trees, k-nearest neighbors, naive Bayes, and support vector machines.

These techniques are called problem transformation or algorithm adaptation techniques. Another way to solve a multiclass classification problem is to use a one-vs-rest strategy, where each class is represented by a binary classification model that is fitted to the data. This is an effective approach for small or moderately imbalanced datasets, but it can be computationally expensive if the number of classes is very high, so we only recommend this approach for simple and straightforward problems. The scikit-learn library provides a OneVsRestClassifier class that allows you to implement this strategy with any binary classifier, such as SVM or Logistic Regression.