Essential Data Science Tips: How to Use OneVsRest and OneVsOne for MultiClass Classification
Classification, as a predictive model, involves aligning each class label to examples. Algorithms designed for binary classification cannot be applied to multiclass classification problems. For such situations, heuristic methods come in handy.
By Thomas Glare, Machine Learning professional and writer.
Only a few classification models aid multiclass classification. Specific algorithms, including logistic regression and perceptron, work best with binary classification and do not support more than two classes of classification tasks. The best alternative for solving multiclass classification problems is splitting the multiclass datasets into multiple binary assemblies of data that can fit the binary classification model.
Algorithms used in binary classification problems cannot work with multiclass tasks. Therefore, heuristic methods, such as onevsone and onevsrest, are used to split multiclass problems into multiple binary datasets and train the binary classification model.
Binary vs. MultiClass Classification
Classification problems are common in machine learning. In most cases, developers prefer using a supervised machinelearning approach to predict class tables for a given dataset. Unlike regression, classification involves designing the classifier model and training it to input and categorize the test dataset. For that, you can divide the dataset into either binary or multiclass modules.
As the name suggests, binary classification involves solving a problem with only two class labels. This makes it easy to filter the data, apply classification algorithms, and train the model to predict outcomes. On the other hand, multiclass classification is applicable when there are more than two class labels in the input train data. The technique enables developers to categorize the test data into multiple binary class labels.
That said, while binary classification requires only one classifier model, the one used in the multiclass approach depends on the classification technique. Below are the two models of the multiclass classification algorithm.
OneVsRest Classification Model for MultiClass Classification
Also known as onevsall, the onevsrest model is a defined heuristic method that leverages a binary classification algorithm for multiclass classifications. The technique involves splitting a multiclass dataset into multiple sets of binary problems. Following this, a binary classifier is trained to handle each binary classification model with the most confident one making predictions.
For instance, with a multiclass classification problem with red, green, and blue datasets, binary classification can be categorized as follows:
 Problem one: red vs. green/blue
 Problem two: blue vs. green/red
 Problem three: green vs. blue/red
The only challenge of using this model is that you should create a model for every class. The three classes require three models from the above datasets, which can be challenging for large sets of data with million rows, slow models, such as neural networks and datasets with a significant number of classes.
The onevsrest approach requires individual models to prognosticate the probabilitylike score. The class index with the largest score is then used to predict a class. As such, it is commonly used for classification algorithms that can naturally predict scores or numerical class membership such as perceptron and logistic regression.
OneVsOne Classification Model for MultiClass Classification
Like the onevsall model, the onevsone is another excellent heuristic method that takes advantage of the binary classification algorithm for classifying multiclass datasets. It also splits multiclass datasets into binary classification problems. However, unlike the onevsrest model that breaks datasets into a single binary assembly of data for every class, the onevsone classification model groups datasets into one data file for every class versus every other class.
For instance, taking into consideration multiclass dataset problems with four classes — blue, red, green and yellow — the onevsone approach splits it into the following six binary classification datasets:
Problem 1: red vs. green
Problem 2: red vs. blue
Problem 3: red vs. yellow
Problem 4: green vs. yellow
Problem 5: blue vs. green
Problem 6: blue vs. yellow
The above are more classification datasets compared to the onevsall approach explained before. As such, the formula used for calculating the total number of binary classification datasets becomes:
The number of classes x (number of classes – 1)/2
From the four multiclass datasets above, this formula gives the expected six binary classification problems as follows:
4 x (4  1)/2
(4 x 3) /2
12/2
=6
While each binary classification model, can predict a single class label, the onevsone strategy predicts the model with the most votes. If the binary classification models predict numerical class memberships accurately, such as probabilities, the class with the most sum score is taken as the class label.
Essentially, this model is best to support the classification of support vector machines and associated kernelbased classification algorithms. This is probably because kernel methods don’t scale in proportion to the size of the training dataset. Also, using the subsets of training data can revert this effect.
The support vector learning machine implementation within Scikitlearn facilitated by the SVC class supports the onevsone classification method of multiclass classification. To use this, you will have to change the settings on the “decision function shape” provision into “ovo.”
Additionally, the scikitlearn archive also allows for a separate onevsone classifier multiclass approach, making the onevsone option usable with any classifier. It enables this multiclass approach to be used with any other binary classifier, such as perceptron, logistic regression, SVM, or different classifiers supporting multiclass classification natively.
Bottom Line
As mentioned, using the onevsrest multiclass classification option makes it challenging to handle large datasets due to a large number of class instances. However, the onevsone multiclass classification option only splits the primary dataset into a single binary classification for each pair of classes.
Although the onevsrest approach cannot handle multiple datasets, it trains less number of classifiers, making it a faster option and often preferred. On the other hand, the onevsone approach is less prone to creating an imbalance in the dataset due to dominance in specific classes.
That said, which one do you think provides the best approach of the two? Share your thoughts in the comments section below.
Bio: Thomas Glare is a machinelearning professional who teaches developers how to get the best from modern machine learning methods and handson tutorials. You can find his articles on this website.
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