prostate

datasets

Easily build and evaluate machine learning models on a dataset. Machine
learning models supported include penalized linear models, penalized linear models
with interactions, random forest, support vector machines, neural networks,
and deep neural networks.

WooYoung Ahn

easyml

Easily Build and Evaluate Machine Learning Models

Woo-Young Ahn

Paul Hendricks

OSU-CCSL 

prostate function

Data frame with columns<dl class="dl-horizontal">
<dt>lcavol</dt><dd>log(cancer volume)</dd>
<dt>lweight</dt><dd>log(prostate weight)</dd>
<dt>age</dt><dd>age</dd>
<dt>lbph</dt><dd>log(benign prostatic hyperplasia amount)</dd>
<dt>svi</dt><dd>seminal vesicle invasion</dd>
<dt>lcp</dt><dd>log(capsular penetration)</dd>
<dt>gleason</dt><dd>Gleason score</dd>
<dt>pgg45</dt><dd>percentage Gleason scores 4 or 5</dd>
<dt>lpsa</dt><dd>log(prostate specific antigen)</dd>
</dl>

Format

Prostate data. — prostate

Data frame with columns<dl class='dl-horizontal'>
<dt>lcavol</dt><dd>log(cancer volume)</dd>
<dt>lweight</dt><dd>log(prostate weight)</dd>
<dt>age</dt><dd>age</dd>
<dt>lbph</dt><dd>log(benign prostatic hyperplasia amount)</dd>
<dt>svi</dt><dd>seminal vesicle invasion</dd>
<dt>lcp</dt><dd>log(capsular penetration)</dd>
<dt>gleason</dt><dd>Gleason score</dd>
<dt>pgg45</dt><dd>percentage Gleason scores 4 or 5</dd>
<dt>lpsa</dt><dd>log(prostate specific antigen)</dd>
</dl>

Prostate data.

prostate: Prostate data.

Description

Usage

Arguments

Format

See Also

Examples