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using modern best practices. It is based on research
in to deep learning best practices undertaken
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Interface to 'fastai'

package-ubuntu-20.04-r-4.0.5#cran#fastai-2.0.7

Jaccard score for multi-label classification problems

JaccardMulti(
  thresh = 0.5,
  sigmoid = TRUE,
  labels = NULL,
  pos_label = 1,
  average = "macro",
  sample_weight = NULL
)

<table summary="R argblock">
<tr valign="top">
<td><code>thresh</code></td>
<td>
thresh
</td>
</tr>
<tr valign="top">
<td><code>sigmoid</code></td>
<td>
sigmoid
</td>
</tr>
<tr valign="top">
<td><code>labels</code></td>
<td>
labels
</td>
</tr>
<tr valign="top">
<td><code>pos_label</code></td>
<td>
pos_label
</td>
</tr>
<tr valign="top">
<td><code>average</code></td>
<td>
average
</td>
</tr>
<tr valign="top">
<td><code>sample_weight</code></td>
<td>
sample_weight
</td>
</tr>
</table>

package-ubuntu-20.04-r-4.0.5#cran#fastai-2.0.7#docs#JaccardMulti

JaccardMulti

Description

Usage

Arguments

Value

fastai

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`thresh`	thresh
`sigmoid`	sigmoid
`labels`	labels
`pos_label`	pos_label
`average`	average
`sample_weight`	sample_weight