DaMiR.ModelSelect {DaMiRseq} | R Documentation

## Select the best classification model

### Description

This function selects the bestmodels learned by the DaMiR.EnsL_Train and a tested by DaMiR.EnsL_Test.

### Usage

    DaMiR.ModelSelect(df, type.sel = c("mode", "median", "greater"),
      th.sel = 0.85, npred.sel = c("min", "rnd"), metric.idx = 1,
      npred.idx = 2)

### Arguments

`df` | A data frame of performance metrics. At least two columns representing a specific classification metrics (e.g., Accuracy) and the number of predictors must be provided. Additionally, other classification metrics (e.g., MCC, Sensitivity, Specificity,PPV, NPV, AUC, ...) can be appended (from the third column onwards) and used for the evaluation, by correctly setting the 'metric.idx' parameter.
`type.sel` | The method to select the best models. Only "mode","median" and "greater" values are allowed. For a specific classification metrics, "mode" selects all models whose score is the mode of all scores; "median" selects all models whose score is the median of all scores; and, "greater" selects all models whose score is greater than the value specified in "th.sel". Default: "mode".
`th.sel` | Threshold for the evaluation of the performance when "type.sel" is equal to "greater". Default: 0.85
`npred.sel` | The method to select the best model. Only "min" and "rnd" values are allowed. Taking into account the subset of models found by 'type.sel', this parameter selects one single model with the minimum number of predictors ("min") or randomly ("rnd"). Default: "min".
`metric.idx` | The index of the 'df' column (i.e., classification metrics) to be considered for the models evaluation. Default: 1.
`npred.idx` | The index of the 'df' column representing the number of predictors. Default: 2.

### Details

This function finds the best model, taking into account specific classification metrics.

### Value

The index of df (row), representing the model selected and a bubble chart

### Author(s)

Mattia Chiesa, Luca Piacentini

### Examples

    # use example data:
    set.seed(1)

[Package DaMiRseq version 2.2.0 Index]