Explain"

In explore: Simplifies Exploratory Data Analysis

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

The explore package offers a simplified way to use machine learning to understand and explain patterns in the data.

• explain_tree() creates a decision tree. The target can be binary, categorical or numerical
• explain_forest() creates a random forest. The target can be binary, categorical or numerical
• explain_logreg() creates a logistic regression. The target must be binary
• balance_target() to balance a target
• weight_target() to create weights for the decision tree

We use synthetic data in this example

library(dplyr)
library(explore)

data <- create_data_buy(obs = 1000)
glimpse(data)

Decision Tree

data %>% explain_tree(target = buy)

data %>% explain_tree(target = mobiledata_prd)

data %>% explain_tree(target = age)

Random Forrest

data %>% explain_forest(target = buy, ntree = 100)

Logistic Regression

data %>% explain_logreg(target = buy)

Balance Target

If you have a data set with a very unbalanced target (in this case only 5% of all observations have buy == 1) it may be difficult to create a decision tree.

data <- create_data_buy(obs = 2000, target1_prob = 0.05)
data %>% describe(buy)

It may help to balance the target before growing the decision tree (or use weighs as alternative). In this example we down sample the data so buy has 10% of target == 1.

data %>%
  balance_target(target = buy, min_prop = 0.10) %>%
  explain_tree(target = buy)

explore documentation built on Oct. 11, 2023, 9:07 a.m.