## What is hyperplane in machine learning?

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Hyperplanes are decision boundaries that help classify the data points. Data points falling on either side of the hyperplane can be attributed to different classes. Also, the dimension of the hyperplane depends upon the number of features.

## What is SVC in machine learning?

The objective of a Linear SVC (Support Vector Classifier) is to fit to the data you provide, returning a “best fit” hyperplane that divides, or categorizes, your data. From there, after getting the hyperplane, you can then feed some features to your classifier to see what the “predicted” class is.

**Why are hyperplanes useful?**

In convex geometry, two disjoint convex sets in n-dimensional Euclidean space are separated by a hyperplane, a result called the hyperplane separation theorem. In machine learning, hyperplanes are a key tool to create support vector machines for such tasks as computer vision and natural language processing.

### Is hyperplane a closed set?

If H is a hyperplane, then H=ϕ−1({0}) for some affine function ϕ. Since {0} is closed, and the preimage of any closed subset under a continuous function is closed, we have that H is closed.

### What is Support Vector Machine geeks for geeks?

Support Vector Machine (SVM) is a relatively simple Supervised Machine Learning Algorithm used for classification and/or regression. It is more preferred for classification but is sometimes very useful for regression as well. Basically, SVM finds a hyper-plane that creates a boundary between the types of data.

**What is Support Vector Machine in simple words?**

SVM or Support Vector Machine is a linear model for classification and regression problems. It can solve linear and non-linear problems and work well for many practical problems. The idea of SVM is simple: The algorithm creates a line or a hyperplane which separates the data into classes.

#### Is SVM same as SVC?

LinearSVC. Linear Support Vector Classification. Similar to SVC with parameter kernel=’linear’, but implemented in terms of liblinear rather than libsvm, so it has more flexibility in the choice of penalties and loss functions and should scale better to large numbers of samples.

#### What is SVC and SVM?

The SVM module (SVC, NuSVC, etc) is a wrapper around the libsvm library and supports different kernels while LinearSVC is based on liblinear and only supports a linear kernel. So: SVC(kernel = ‘linear’) is in theory “equivalent” to: LinearSVC()