Decision Tree Party Package Prediction Error - Levels Do Not Match
In this article, we will delve into the world of decision trees and explore a common issue that arises when working with the party package in R. The problem at hand is related to the levels of factors in the testing dataset not matching those in the training dataset, leading to an error when making predictions.
Introduction
Decision trees are a popular machine learning algorithm used for both classification and regression tasks. They work by recursively partitioning the data into smaller subsets based on the most informative feature. The party package provides an implementation of decision trees that is specifically designed for working with categorical variables.
One of the key benefits of using the party package is its ability to handle categorical variables in a flexible and efficient manner. However, when making predictions, it’s not uncommon to encounter issues related to the levels of factors in the testing dataset not matching those in the training dataset.
Background
Before we dive into the solution, let’s take a closer look at the party package and its implementation of decision trees.
The party package provides an interface for working with decision trees using the ctree() function. This function takes in a formula specifying the dependent variable and the predictor variables, as well as a dataset object containing the data to be used for training and prediction.
When making predictions using the predict() function, the party package checks whether the levels of factors in the testing dataset match those in the training dataset. If there are any discrepancies, it will throw an error indicating that the levels do not match.
The Problem
The problem arises when we try to make predictions using a decision tree model trained on one dataset and test data from another dataset with different levels for the same factor variables.
For example, let’s consider the following code:
# create training data sample
data_train <- data.frame(Rate = c(1.5, 0.6, 3, 2.1, 1.1),
Bank = c("A", "B", "C", "D", "E"),
Product = c("aaa", "abc", "bac", "cba", "cca"),
Salary = c(100000, 60000, 10000, 50000, 80000))
# create testing data sample
data_test <- data.frame(Rate = c(2.0, 0.5, 0.8, 2.1),
Bank = c("A", "D", "E", "C"),
Product = c("cba", "cca", "cba", "abc"),
Salary = c(80000, 250000, 120000, 65000))
# fit the model
mytree <- ctree(Rate ~ Bank + Product + Salary, data = data_train)
In this example, we create a training dataset with different levels for Bank and Product, as well as a testing dataset with different levels for the same factors.
When we try to make predictions using the predict() function, we encounter an error indicating that the levels do not match:
# generate predictions
fit1 <- predict(mytree, newdata = data_test)
The Solution
So, how can we resolve this issue? One possible solution is to rebuild the factors in the testing dataset using comparable levels instead of assigning new levels to existing factors.
Let’s take a closer look at the party package documentation and explore the different ways in which we can handle this issue:
# get the union of levels between train and test for Bank and Product
bank_levels <- union(levels(data_test$Bank), levels(data_train$Bank))
product_levels <- union(levels(data_test$Product), levels(data_train$Product))
# rebuild Bank with union of levels
data_test$Bank <- with(data_test, factor(Bank, levels = bank_levels))
data_train$Bank <- with(data_train, factor(Bank, levels = bank_levels))
# rebuild Product with union of levels
data_test$Product <- with(data_test, factor(Product, levels = product_levels))
data_train$Product <- with(data_train, factor(Product, levels = product_levels))
In this code, we first get the union of levels between the training and testing datasets for Bank and Product. We then rebuild the factors in the testing dataset using these comparable levels.
By rebuilding the factors in this way, we ensure that the levels of factors in the testing dataset match those in the training dataset, resolving the issue with predictions.
Example Use Case
Here’s an example use case where we can demonstrate how to rebuild the factors in the testing dataset using comparable levels:
# create training data sample
data_train <- data.frame(Rate = c(1.5, 0.6, 3, 2.1, 1.1),
Bank = c("A", "B", "C", "D", "E"),
Product = c("aaa", "abc", "bac", "cba", "cca"),
Salary = c(100000, 60000, 10000, 50000, 80000))
# create testing data sample
data_test <- data.frame(Rate = c(2.0, 0.5, 0.8, 2.1),
Bank = c("A", "D", "E", "C"),
Product = c("cba", "cca", "cba", "abc"),
Salary = c(80000, 250000, 120000, 65000))
# rebuild factors using comparable levels
bank_levels <- union(levels(data_test$Bank), levels(data_train$Bank))
product_levels <- union(levels(data_test$Product), levels(data_train$Product))
data_test$Bank <- with(data_test, factor(Bank, levels = bank_levels))
data_train$Bank <- with(data_train, factor(Bank, levels = bank_levels))
data_test$Product <- with(data_test, factor(Product, levels = product_levels))
data_train$Product <- with(data_train, factor(Product, levels = product_levels))
# fit the model
mytree <- ctree(Rate ~ Bank + Product + Salary, data = data_train)
# generate predictions
fit1 <- predict(mytree, newdata = data_test)
In this example, we create a training dataset and a testing dataset with different levels for Bank and Product. We then rebuild the factors in the testing dataset using comparable levels and fit the decision tree model. Finally, we make predictions using the predict() function.
By rebuilding the factors in this way, we ensure that the levels of factors in the testing dataset match those in the training dataset, resolving the issue with predictions.
Last modified on 2023-09-29