train


The data is from a historical marketing campaign.
It contains 68 predictive variables and 4,972 records. In addition, it contains a
treatment indicator and a purchase indicator.


datasets

Performs exploratory data analysis and variable screening for
binary classification models using weight-of-evidence (WOE) and information
value (IV). In order to make the package as efficient as possible, aggregations
are done in data.table and creation of WOE vectors can be distributed across
multiple cores. The package also supports exploration for uplift models (NWOE
and NIV).

Larsen Kim

Information

Data Exploration with Information Theory (Weight-of-Evidence and
Information Value)

train function

A data frame with 10000 rows and 70 variables:
<ul>
<li> TREATMENT: equals 1 if the person received the marketing offer, and 0 if the person was in the control group
  </li>
<li> PURCHASE: equals 1 if the person accepted the offer, and 0 otherwise
  </li>
<li> UNIQUE_ID: unique identifier
  </li>
<li> AGE: age of the person
  </li>
<li> D_REGION_X: 1 if the person lives in region X, 0 otherwise (3 regions: A, B, C)
  </li>
<li> Other variables are from credit bureau data (e.g., N_OPEN_REV_ACTS = number of open revolving accounts)
</li>
</ul>

train: Training dataset

Description

Usage

Arguments

Format