extract

[.SurvMatrix

[,Resamples,ANY,ANY,ANY-method

<code><a rd-options="" href="/link/SurvMatrix?package=MachineShop&version=1.6.0" data-mini-rdoc="MachineShop::SurvMatrix">SurvMatrix</a></code> object or <a rd-options="" href="/link/resample?package=MachineShop&version=1.6.0" data-mini-rdoc="MachineShop::resample">resample</a> result from which
to extract elements.

indices specifying elements to extract.

i, j

logical indicating that the result be returned as a
<code>numeric</code> coerced to the lowest dimension possible if <code>TRUE</code> or
retained as the original 2-dimensional object class otherwise.

drop

Operators acting on data structures to extract parts.

Meta-package for statistical and machine learning with a unified interface for model fitting, prediction, performance assessment, and presentation of results.  Approaches for model fitting and prediction of numerical, categorical, or censored time-to-event outcomes include traditional regression models, regularization methods, tree-based methods, support vector machines, neural networks, ensembles, data preprocessing, filtering, and model tuning and selection.  Performance metrics are provided for model assessment and can be estimated with independent test sets, split sampling, cross-validation, or bootstrap resampling.  Resample estimation can be executed in parallel for faster processing and nested in cases of model tuning and selection.  Modeling results can be summarized with descriptive statistics; calibration curves; variable importance; partial dependence plots; confusion matrices; and ROC, lift, and other performance curves.

Brian Smith

MachineShop

Machine Learning Models and Tools

extract function

<code><a rd-options='' href='SurvMatrix'>SurvMatrix</a></code> object or <a rd-options='' href='resample'>resample</a> result from which
to extract elements.

extract: Extract Parts of an Object

Description

Usage

Arguments