Table of Contents

## What is LIBSVM algorithm?

LIBSVM implements the Sequential minimal optimization (SMO) algorithm for kernelized support vector machines (SVMs), supporting classification and regression. LIBLINEAR implements linear SVMs and logistic regression models trained using a coordinate descent algorithm.

## What is the LIBSVM format?

MLlib supports reading training examples stored in LIBSVM format, which is the default format used by LIBSVM and LIBLINEAR . It is a text format in which each line represents a labeled sparse feature vector using the following format: label index1:value1 index2:value2 …

**What does Liblinear mean?**

Library for Large Linear Classification

liblinear — Library for Large Linear Classification. Uses a coordinate descent algorithm. Coordinate descent is based on minimizing a multivariate function by solving univariate optimization problems in a loop. In other words, it moves toward the minimum in one direction at a time.

**What is the type of SVM learning?**

“Support Vector Machine” (SVM) is a supervised machine learning algorithm that can be used for both classification or regression challenges.

### What is LIBSVM format in spark?

libsvm package implements Spark SQL data source API for loading LIBSVM data as DataFrame . The loaded DataFrame has two columns: label containing labels stored as doubles and features containing feature vectors stored as Vector s.

### How do you cite LIBSVM?

Citation in APA style -J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3), 1–27.

**How do I run LIBSVM in Matlab?**

Here are the steps to run libsvm on MATLAB:

- Download it from here.
- Add the svmtrain, svmpredict, libsvmwrite & libscmread . mex Files to your matlab path (probably you just put them in the working folder…)

**Which is better L1 or L2 regularization?**

From a practical standpoint, L1 tends to shrink coefficients to zero whereas L2 tends to shrink coefficients evenly. L1 is therefore useful for feature selection, as we can drop any variables associated with coefficients that go to zero. L2, on the other hand, is useful when you have collinear/codependent features.

#### What are types of regularization?

There are two types of regularization as follows:

- L1 Regularization or Lasso Regularization.
- L2 Regularization or Ridge Regularization.

#### What regularization means?

transitive verb. : to make regular by conformance to law, rules, or custom.

**What is SVM best for?**

SVMs are used in applications like handwriting recognition, intrusion detection, face detection, email classification, gene classification, and in web pages. This is one of the reasons we use SVMs in machine learning. It can handle both classification and regression on linear and non-linear data.

**What is SVM function?**

Support Vector Machine or SVM is one of the most popular Supervised Learning algorithms, which is used for Classification as well as Regression problems. However, primarily, it is used for Classification problems in Machine Learning.

## How do you cite Libsvm?

## What is Rho in SVM?

rho is the bias term in the decision function sgn(w^Tx – rho). nSV and nBSV are number of support vectors and bounded support vectors (i.e., alpha_i = C). nu-svm is a somewhat equivalent form of C-SVM where C is replaced by nu. nu simply shows the corresponding parameter.

**Why do we need L2 regularization?**

The whole purpose of L2 regularization is to reduce the chance of model overfitting. There are other techniques that have the same purpose. These anti-overfitting techniques include dropout, jittering, train-validate-test early stopping and max-norm constraints.