evaluate_pred

This function compares the prediction results returned by a classifier with ground truth, 
and finally gives a summary of the evaluation.

Efficient Bayesian multinomial logistic regression based on heavy-tailed
(hyper-LASSO, non-convex) priors. The posterior of coefficients and hyper-parameters
is sampled with restricted Gibbs sampling for leveraging the high-dimensionality and
Hamiltonian Monte Carlo for handling the high-correlation among coefficients. A detailed
description of the method: Li and Yao (2018),
Journal of Statistical Computation and Simulation, 88:14, 2827-2851, <arXiv:1405.3319>.

Longhai Li

HTLR

Bayesian Logistic Regression with Heavy-Tailed Priors

Steven Liu

evaluate_pred function

<dl><dt>y.pred</dt>
<dd>A matrix of predicted probabilities, as returned by a classifier.</dd>
<dt>y.true</dt>
<dd>Ground truth labels vector.</dd>
<dt>caseid</dt>
<dd>The names of test cases which we take account of. By default all test cases are used for evaluation.</dd>
<dt>showplot</dt>
<dd>Logical; if <code>TRUE</code>, a summary plot will be generated.</dd>
<dt>...</dt>
<dd>Not used.</dd></dl>

Arguments

Evaluate Prediction Results — evaluate_pred

<dl>

<dt>y.pred</dt>
<dd>A matrix of predicted probabilities, as returned by a classifier.</dd>


<dt>y.true</dt>
<dd>Ground truth labels vector.</dd>


<dt>caseid</dt>
<dd>The names of test cases which we take account of. By default all test cases are used for evaluation.</dd>


<dt>showplot</dt>
<dd>Logical; if <code>TRUE</code>, a summary plot will be generated.</dd>


<dt>...</dt>
<dd>Not used.</dd>

</dl>

evaluate_pred: Evaluate Prediction Results

Description

Usage

Value

Arguments