get_auc_ks_lambda

A lasso model genereted by glmnet.

lasso_model

x_test

A matrix of y test dataset with y.

y_test

Logical, save results in locally specified folder. Default is FALSE

save_data

Logical, if TRUE plot the results. Default is TRUE.

plot_show

The name for periodically saved results files. Default is NULL.

file_name

The path for periodically saved results files.

dir_path

get_auc_ks_lambda
<code>get_auc_ks_lambda</code> is for get best lambda required in lasso_filter. This function required in <code>lasso_filter</code>


Provides a toolkit for building predictive models in one integrated offering. Contains infrastructure functionalities such as data exploration and preparation, missing values treatment, outliers treatment, variable derivation, variable selection, dimensionality reduction, grid search for hyperparameters, data mining and visualization, model evaluation, strategy analysis etc. 'creditmodel' 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.Anderson, R. (2007). The credit scoring toolkit: Theory and practice for retail credit risk management and decision automation.
2.Find, S. (2012, ISBN13: 9780230347762). Credit scoring, response modelling and insurance rating:A practical guide to forecasting consumer behaviour.

Dongping Fan

creditmodel

Build Binary Classification Models in One Integrated Offering

get_auc_ks_lambda function

get_auc_ks_lambda
<code>get_auc_ks_lambda</code> is for get best lambda required in lasso_filter. This function required in <code>lasso_filter</code> — get_auc_ks_lambda

get_auc_ks_lambda: get_auc_ks_lambda `get_auc_ks_lambda` is for get best lambda required in lasso_filter. This function required in `lasso_filter`

Description

Usage

Arguments

Value

See Also