buildExplainer: Step 1: Build an lightgbmExplainer

In lantanacamara/lightgbmExplainer: An R package that makes lightgbm models fully interpretable

Description

This function outputs an lightgbmExplainer (a data table that stores the feature impact breakdown for each leaf of each tree in an lightgbm model). It is required as input into the explainPredictions and showWaterfall functions.

Usage

buildExplainer(lgb_tree)

Arguments

lgb_tree

A lightgbm.dt.tree

Value

The lightgbm Explainer for the model. This is a data table where each row is a leaf of a tree in the lightgbm model and each column is the impact of each feature on the prediction at the leaf.

The leaf and tree columns uniquely identify the node.

The sum of the other columns equals the prediction at the leaf (log-odds if binary response).

The 'intercept' column is identical for all rows and is analogous to the intercept term in a linear / logistic regression.

Examples

library(lightgbm) # v2.1.0 or above
library(lightgbmExplainer)

# Load Data
data(agaricus.train, package = "lightgbm")
# Train a model
lgb.dtrain <- lgb.Dataset(agaricus.train$data, label = agaricus.train$label)
lgb.params <- list(objective = "binary")
lgb.model <- lgb.train(lgb.params, lgb.dtrain, 5)
# Build Explainer
lgb.trees <- lgb.model.dt.tree(lgb.model) # First get a lgb tree
explainer <- buildExplainer(lgb.trees)
# compute contribution for each data point
pred.breakdown <- explainPredictions(lgb.model, explainer, agaricus.train$data)
# Show waterfall for the 8th observation
showWaterfall(lgb.model, explainer, lgb.dtrain, agaricus.train$data,  8, type = "binary")

lantanacamara/lightgbmExplainer documentation built on Aug. 8, 2019, 4 p.m.