Statistical models such as Perceptron, Logistic Regression and Support Vector Machine are designed to classify two classes at a time and do not natively support classification tasks with more than two classes.

*So, to Implement multiclass classification in the above models, We usually split the multi-class classification dataset into multiple binary classification datasets and fit a binary classification model on each. This leads to two different meta strategies — OVR and OVO*

**One vs Rest (OVR)**

*OVR strategy splits a multi-class classification into one binary classification problem per class.*

For eg- If we want to classify Red, Blue and Green

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*Decision Tree is a type of supervised learning algorithm that can be used in both regression and classification problems.*

**Goal in Decision Tree**

Goal in Decision tree is to create a training model which can be used to predict the value or the class of the target variable by learning simple decision rules and terminologies inferred from a training data.

**Idea behind Decision Tree**

At each point or a training sample, We consider a set of questions that can partition our dataset.

We choose the question that provides the best split and again find the best question for the partition.

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SVM ( Support Vector Machines ) is a supervised machine learning algorithm which can be used for both classification and regression challenges. But, It is widely used in classification problems.

In SVM, we plot each data item as a point in n-dimensional space (where n = no of features in a dataset) with the value of each feature being the value of a particular coordinates. Such that, value of feature is equal to the value of coordinate then we perform classification by finding the most appropriate hyperplane that differentiates two classes very well.

Our goal is to determine some establishing…