rf_params

Number of trees to grow. This should not be set to too small a number, to ensure that every input row gets predicted at least a few times.

ntree

Minimum size of terminal nodes. Setting this number larger causes smaller trees to be grown (and thus take less time). Note that the default values are different for classification (1) and regression (5).

nodesize

Percentage of sample to draw. Default is 0.2.

samp_rate

A logical.If TRUE, then tune Random Forest model.Default is FALSE.

tune_rf

<code>rf_params</code> is the list of parameters to train a Random Forest using in <code><a rd-options="" href="/link/training_model?package=creditmodel&version=1.3.1" data-mini-rdoc="creditmodel::training_model">training_model</a></code>.

Provides a highly efficient R tool suite for Credit Modeling, Analysis and Visualization.Contains infrastructure functionalities such as data exploration and preparation, missing values treatment, outliers treatment, variable derivation, variable selection, dimensionality reduction, grid search for hyper parameters, data mining and visualization, model evaluation, strategy analysis etc. This package is designed to make the development of binary classification models (machine learning based models as well as credit scorecard) simpler and faster. The references including: 1 Refaat, M. (2011, ISBN: 9781447511199). Credit Risk Scorecard: Development and Implementation Using SAS; 2 Bezdek, James C.FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences (0098-3004),<DOI:10.1016/0098-3004(84)90020-7>.

Dongping Fan

creditmodel

Toolkit for Credit Modeling, Analysis and Visualization

rf_params function

<code>rf_params</code> is the list of parameters to train a Random Forest using in <code><a rd-options='' href='training_model'>training_model</a></code>.

rf_params: Random Forest Parameters

Description

Usage

Arguments

Value

Details

See Also