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For simplicity, we will assume that we have only two independent variables.Ī hyper-plan is a plane in space that is used to separate the two data points classes. Let’s assume that we have the data points as shown in the diagram below. Support Vector Machine is a supervised classification ML algorithm that is used to classify data points into two classes by finding the distance between data points groups and maximizing the gap between them.Ī supervised ML algorithm means that the data set used to train the model is already labeled and the main task of the model is to classify the data points, unlike the unsupervised algorithms, which are based on unlabeled data sets, and find the categories of the classification with the relations between features. In this blog, we will try to understand the “ Support Vector Machine” algorithm and the mathematics that the algorithm was built on it. The main points to understand any machine learning model is, how the model fits the data, what are the hyperparameters used in the model and how the model predicts the test data? It’s crucial to understand how they work.
![hyperplan equation hyperplan equation](https://miro.medium.com/max/1280/1*7nv__ChFcsjPQrcpDGZMmA.png)
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