fftrees_threshold_factor_grid

Perform a grid search over factor and return accuracy statistics for a given factor cue

Create, visualize, and test fast-and-frugal decision trees (FFTs) using the algorithms and methods described in Phillips, Neth, Woike & Gaissmaier (2017).
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 communicate, and are robust against overfitting.

Hansjoerg Neth

FFTrees

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

Nathaniel Phillips

Jan Woike

Wolfgang Gaissmaier

fftrees_threshold_factor_grid function

<dl><dt>thresholds</dt>
<dd>numeric. A vector of factor thresholds to consider.</dd>
<dt>cue_v</dt>
<dd>numeric. Feature/cue values.</dd>
<dt>criterion_v</dt>
<dd>logical. A logical vector of (TRUE) criterion values.</dd>
<dt>directions</dt>
<dd>character. Character vector of threshold directions to consider.</dd>
<dt>sens.w</dt>
<dd>numeric. Sensitivity weight parameter (from 0 to 1, for computing <code>wacc</code>).
Default: <code>sens.w = .50</code>.</dd>
<dt>cost.outcomes</dt>
<dd>list. A list of length 4 with names 'hi', 'fa', 'mi', and 'cr' specifying
the costs of a hit, false alarm, miss, and correct rejection, respectively.
For instance, <code>cost.outcomes = listc("hi" = 0, "fa" = 10, "mi" = 20, "cr" = 0)</code> means that
a false alarm and miss cost 10 and 20, respectively, while correct decisions have no cost.</dd>
<dt>cost.each</dt>
<dd>numeric.</dd>
<dt>goal.threshold</dt>
<dd>character.</dd></dl>

Arguments

Perform a grid search over factor and return accuracy statistics for a given factor cue — fftrees_threshold_factor_grid

<dl>

<dt>thresholds</dt>
<dd>numeric. A vector of factor thresholds to consider.</dd>


<dt>cue_v</dt>
<dd>numeric. Feature/cue values.</dd>


<dt>criterion_v</dt>
<dd>logical. A logical vector of (TRUE) criterion values.</dd>


<dt>directions</dt>
<dd>character. Character vector of threshold directions to consider.</dd>


<dt>sens.w</dt>
<dd>numeric. Sensitivity weight parameter (from 0 to 1, for computing <code>wacc</code>).
Default: <code>sens.w = .50</code>.</dd>


<dt>cost.outcomes</dt>
<dd>list. A list of length 4 with names 'hi', 'fa', 'mi', and 'cr' specifying
the costs of a hit, false alarm, miss, and correct rejection, respectively.
For instance, <code>cost.outcomes = listc("hi" = 0, "fa" = 10, "mi" = 20, "cr" = 0)</code> means that
a false alarm and miss cost 10 and 20, respectively, while correct decisions have no cost.</dd>


<dt>cost.each</dt>
<dd>numeric.</dd>


<dt>goal.threshold</dt>
<dd>character.</dd>

</dl>

fftrees_threshold_factor_grid: Perform a grid search over factor and return accuracy statistics for a given factor cue

Description

Usage

Value

Arguments

See Also