banana

banana-bc.train

banana-bc.test

banana-mc.train

banana-mc.test

<p>Generated data set having a binary or 4-level Y variable and
a two-dimensional X (first two levels resemble bananas).
Both the train and the test set have 2000 samples in the binary case,
and 4000 in the multi-class case.
They were generated by the authors and their collaborators.</p>

Support vector machines (SVMs) and related kernel-based learning
algorithms are a well-known class of machine learning algorithms, for
non-parametric classification and regression.
liquidSVM is an implementation of SVMs whose key features are:
fully integrated hyper-parameter selection,
extreme speed on both small and large data sets,
full flexibility for experts, and
inclusion of a variety of different learning scenarios:
multi-class classification, ROC, and Neyman-Pearson learning, and
least-squares, quantile, and expectile regression.

Philipp Thomann

liquidSVM

A Fast and Versatile SVM Package

banana function

<code>banana-bc.train</code>, <code>banana-bc.test</code>
<code>banana-mc.train</code>, and <code>banana-mc.test</code> — banana

<code>banana-bc.train</code>, <code>banana-bc.test</code>
<code>banana-mc.train</code>, and <code>banana-mc.test</code>

banana: `banana-bc.train`, `banana-bc.test` `banana-mc.train`, and `banana-mc.test`

Description

Arguments