breastcancer

Physiological data of patients tested for breast cancer

datasets

Create, visualize, and test fast-and-frugal decision trees (FFTs) using the algorithms and methods described by Phillips, Neth, Woike & Gaissmaier (2017), <doi:10.1017/S1930297500006239>.
FFTs are simple and transparent decision trees for solving binary classification problems.
FFTs can be preferable to more complex algorithms because they require very little information, are easy to understand and communicate, and are robust against overfitting.

Hansjoerg Neth

FFTrees

Generate, Visualise, and Evaluate Fast-and-Frugal Decision Trees

Nathaniel Phillips

Jan Woike

Wolfgang Gaissmaier

breastcancer function

A data frame containing 699 patients (rows) and 9 variables (columns).
<dl>
 <dt>thickness</dt>
<dd>Clump Thickness</dd> <dt>cellsize.unif</dt>
<dd>Uniformity of Cell Size</dd> <dt>cellshape.unif</dt>
<dd>Uniformity of Cell Shape</dd> <dt>adhesion</dt>
<dd>Marginal Adhesion</dd> <dt>epithelial</dt>
<dd>Single Epithelial Cell Size</dd> <dt>nuclei.bare</dt>
<dd>Bare Nuclei</dd> <dt>chromatin</dt>
<dd>Bland Chromatin</dd> <dt>nucleoli</dt>
<dd>Normal Nucleoli</dd> <dt>mitoses</dt>
<dd>Mitoses</dd> <dt>diagnosis</dt>
<dd>Criterion: Absence/presence of breast cancer.
Values: <code>FALSE</code> vs. <code>TRUE</code> (65.0% vs.\ 35.0%).</dd>
</dl>

Format

Physiological data of patients tested for breast cancer — breastcancer

A data frame containing 699 patients (rows) and 9 variables (columns).
<dl>
 <dt>thickness</dt>
<dd>Clump Thickness</dd>

 <dt>cellsize.unif</dt>
<dd>Uniformity of Cell Size</dd>

 <dt>cellshape.unif</dt>
<dd>Uniformity of Cell Shape</dd>

 <dt>adhesion</dt>
<dd>Marginal Adhesion</dd>

 <dt>epithelial</dt>
<dd>Single Epithelial Cell Size</dd>

 <dt>nuclei.bare</dt>
<dd>Bare Nuclei</dd>

 <dt>chromatin</dt>
<dd>Bland Chromatin</dd>

 <dt>nucleoli</dt>
<dd>Normal Nucleoli</dd>

 <dt>mitoses</dt>
<dd>Mitoses</dd>

 <dt>diagnosis</dt>
<dd>Criterion: Absence/presence of breast cancer.
Values: <code>FALSE</code> vs. <code>TRUE</code> (65.0% vs.\ 35.0%).</dd>


</dl>

breastcancer: Physiological data of patients tested for breast cancer

Description

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Arguments

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Details

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