## What package is KSVM?

Question 2.2: Using the support vector machine function ksvm contained in the R package kernlab, find a good classifier for this data.

**What is Xmatrix KSVM?**

xmatrix : Object of class “input” ( “list” for multiclass problems or “matrix” for binary classification and regression problems) containing the support vectors calculated from the data matrix used during computations (possibly scaled and without NA).

### What is kernel in KSVM?

kernel. the kernel function used in training and predicting. This parameter can be set to any function, of class kernel, which computes the inner product in feature space between two vector arguments (see kernels ).

**What is Kernlab?**

kernlab is an extensible package for kernel-based machine learning methods in R. It takes advantage of R’s new S4 object model and provides a framework for creating and using kernel- based algorithms. Keywords: kernel methods, support vector machines, quadratic programming, ranking, clustering, S4, R.

## What is Library e1071 in R?

e1071 is a package for R programming that provides functions for statistic and probabilistic algorithms like a fuzzy classifier, naive Bayes classifier, bagged clustering, short-time Fourier transform, support vector machine, etc.. It also facilitates probabilistic classification by using the kernel trick.

**What is SVM kernel?**

A kernel is a function used in SVM for helping to solve problems. They provide shortcuts to avoid complex calculations. The amazing thing about kernel is that we can go to higher dimensions and perform smooth calculations with the help of it. We can go up to an infinite number of dimensions using kernels.

### Why kernel function is used?

In machine learning, a “kernel” is usually used to refer to the kernel trick, a method of using a linear classifier to solve a non-linear problem. The kernel function is what is applied on each data instance to map the original non-linear observations into a higher-dimensional space in which they become separable.

**What is the difference between SVM and KSVM?**

Ksvm shows minimum number of support vectors than svm. The comparison of svm and ksvm was made for the twitter data set. The experimental results show that the number of support vectors by ksvm is very low when compare with normal svm.

## What is Gamma SVC?

gamma. gamma is a parameter for non linear hyperplanes. The higher the gamma value it tries to exactly fit the training data set gammas = [0.1, 1, 10, 100]for gamma in gammas: svc = svm.SVC(kernel=’rbf’, gamma=gamma).fit(X, y)

**How do I get the e1071 package in R?**

In R (for OSX) select Packages & Data -> Package Installer from the menu. If you haven’t already select a Datacenter. This is how I did it for mac: 1) Download the binary – https://CRAN.R-project.org/package=e1071 2) Run R CMD INSTALL e1071_version.

### What data does KSVM take?

By default the data is taken from the environment which `ksvm’ is called from. a response vector with one label for each row/component of x. Can be either a factor (for classification tasks) or a numeric vector (for regression). A logical vector indicating the variables to be scaled.

**How does KSVM calculate support vectors for RBF?**

When using an RBF kernel and setting kpar to “automatic”, ksvm uses the sigest function to estimate the quantiles and uses the median of the values. The resulting support vectors, (alpha vector) (possibly scaled).

## How do I create a KSVM object?

Objects can be created by calls of the form new (“ksvm”.) or by calls to the ksvm function. Object of class “character” containing the support vector machine type (“C-svc”, “nu-svc”, “C-bsvc”, “spoc-svc”, “one-svc”, “eps-svr”, “nu-svr”, “eps-bsvr”)

**How does KSVM calculate quantiles in KPAR?**

When using an RBF kernel and setting kpar to “automatic”, ksvm uses the sigest function to estimate the quantiles and uses the median of the values. The resulting support vectors, (alpha vector) (possibly scaled). The index of the resulting support vectors in the data matrix.