train.model {SIAMCAT} | R Documentation

## Model training

### Description

This function trains the a machine learning model on the training data

### Usage

    train.model(siamcat, method = c("lasso", "enet", "ridge", "lasso_ll",
    "ridge_ll", "randomForest"), stratify = TRUE, modsel.crit = list("auc"),
    min.nonzero.coeff = 1, param.set = NULL, perform.fs = FALSE, param.fs =
    list(thres.fs = 100, method.fs = "AUC", direction='absolute'),
    feature.type='normalized', verbose = 1)

### Arguments

`siamcat` | object of class siamcat-class
`method` | string, specifies the type of model to be trained, may be one of these: `c('lasso', 'enet', 'ridge', 'lasso_ll', 'ridge_ll', 'randomForest')`
`stratify` | boolean, should the folds in the internal cross-validation be stratified?, defaults to `TRUE`
`modsel.crit` | list, specifies the model selection criterion during internal cross-validation, may contain these: `c('auc', 'f1', 'acc', 'pr')`, defaults to `list('auc')`
`min.nonzero.coeff` | integer number of minimum nonzero coefficients that should be present in the model (only for `'lasso'`, `'ridge'`, and `'enet'`), defaults to `1`
`param.set` | list, set of extra parameters for mlr run, may contain: `cost` and `class.weights` - for lasso_ll and ridge_ll `alpha` - for enet `ntree` and `mtry` - for RandomForrest. See below for details. Defaults to `NULL`
`perform.fs` | boolean, should feature selection be performed? Defaults to `FALSE`
`param.fs` | list, parameters for the feature selection, must contain: `thres.fs` - threshold for the feature selection, `method.fs` - method for the feature selection, may be `AUC`, `gFC`, or `Wilcoxon` `direction` - for `AUC` and `gFC`, select either the top associated features (independent of the sign of enrichment), the top positively associated featured, or the top negatively associated features, may be `absolute`, `positive`, or `negative`. Will be ignored for `Wilcoxon`. See Details for more information. Defaults to `list(thres.fs=100, method.fs="AUC", direction='absolute')`
`feature.type` | string, on which type of features should the function work? Can be either `"original"`, `"filtered"`, or `"normalized"`. Please only change this paramter if you know what you are doing!
`verbose` | integer, control output: `0` for no output at all, `1` for only information about progress and success, `2` for normal level of information and `3` for full debug information, defaults to `1`

### Details

This functions performs the training of the machine learning model and functions as an interface to the `mlr`-package.

The function expects a siamcat-class-object with a prepared cross-validation (see create.data.split) in the `data_split`-slot of the object. It then trains a model for each fold of the datasplit.

For the machine learning methods that require additional hyperparameters (e.g. `lasso_ll`), the optimal hyperparameters are tuned with the function tuneParams within the `mlr`-package.

The different machine learning methods are implemented as mlr-tasks:

- `'lasso'`, `'enet'`, and `'ridge'` use the `'classif.cvglmnet'` Learner,

- `'lasso_ll'` and `'ridge_ll'` use the `'classif.LiblineaRL1LogReg'` and the `'classif.LiblineaRL2LogReg'` Learners respectively

- `'randomForest'` is implemented via the `'classif.randomForest'` Learner.

Hyperparameters You also have additional control over the machine learning procedure by supplying information through the `param.set` parameter within the function. We encourage you to check out the excellent mlr documentation for more in-depth information.

Here is a short overview which parameters you can supply in which form:

- enet The alpha parameter describes the mixture between lasso and ridge penalty and is -per default- determined using internal cross-validation (the default would be equivalent to `param.set=list('alpha'=c(0,1))`). You can supply either the limits of the hyperparameter exploration (e.g. with limits 0.2 and 0.8: `param.set=list('alpha'=c(0.2,0.8))`) or you can supply a fixed alpha value as well (`param.set=list('alpha'=0.5)`).

- lasso_ll/ridge_ll You can supply both class.weights and the cost parameter (cost of the constraints violation, see LiblineaR for more info). The default values would be equal to `param.set=list('class.weights'=c(5, 1), 'cost'=c(10 ^ seq(-2, 3, length = 6 + 5 + 10))`.

- randomForest You can supply the two parameters ntree (Number of trees to grow) and mtry (Number of variables randomly sampled as candidates at each split). See also randomForest for more info. The default values correspond to `param.set=list('ntree'=c(100, 1000), 'mtry'= c(round(sqrt.mdim / 2), round(sqrt.mdim), round(sqrt.mdim * 2)))` with `sqrt.mdim=sqrt(nrow(data))`.

Feature selection The function can also perform feature selection on each individual fold. At the moment, three methods for feature selection are implemented:

- `'AUC'` - computes the Area Under the Receiver Operating Characteristics Curve for each single feature and selects the top `param.fs$thres.fs`, e.g. 100 features

- `'gFC'` - computes the generalized Fold Change (see check.associations) for each feature and likewise selects the top `param.fs$thres.fs`, e.g. 100 features

- `Wilcoxon` - computes the p-Value for each single feature with the Wilcoxon test and selects features with a p-value smaller than `param.fs$thres.fs`

For `AUC` and `gFC`, feature selection can also be directed, that means that the features will be selected either based on the overall association (`absolute` - `gFC` will be converted to absolute values and `AUC` values below `0.5` will be converted by `1 - AUC`), or on associations in a certain direction (`positive` - positive enrichment as measured by positive values of the `gFC` or `AUC` values higher than `0.5` - and reversely for `negative`).

### Value

object of class siamcat-class with added `model_list`

### Examples

    data(siamcat_example)

    # simple working example
    siamcat_example <- train.model(siamcat_example, method='lasso')

[Package SIAMCAT version 1.10.0 Index]