Understanding the Predict Function in Rpart for Classification Tasks with Numeric Output

Understanding the Predict Function in Rpart

In this article, we will delve into the world of decision trees using the rpart package in R. We will explore how to get numeric output from the predict function instead of factors.

Introduction

Decision trees are a popular machine learning algorithm used for classification and regression tasks. The rpart package is an implementation of the recursive partitioning method, which is widely used for building decision trees. In this article, we will focus on using the predict function in rpart to get the desired output.

Setting Up the Problem

The problem at hand involves building a decision tree using the rpart package to classify the quality of wines based on various factors such as pH, sulfates, and alcalinity. The goal is to use the predict function to get the class labels (fine, tasty, or yucky) for new observations.

# Load required libraries
library(rpart)

# Load the wine dataset
wine <- read.csv("https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv", sep = ";")

# Convert the quality column to a factor
wine$taste <- ifelse(wine$quality < 5, "yucky", "tasty")
wine$taste[wine$quality == 5] <- "fine"
wine$taste <- as.factor(wine$taste)

# Split the data into training and test sets
set.seed(123)
sample <- sample(nrow(wine), 0.7 * nrow(wine))
train <- wine[sample, ]
test <- wine[-sample, ]

# Build a decision tree
DecisionTree <- rpart(taste ~ ., data = train)

# Make predictions on the test set using the predict function
pred <- predict(DecisionTree, test)

The Problem with the Current Output

The current output from the predict function is in the form of factors (fine, tasty, yucky), which are not suitable for calculating accuracy.

# View the first 10 rows of the prediction matrix
head(pred, 10)

   fine tastey yucky
6     1      0     0
14    1      0     0
18    1      0     0
23    1      0     0
24    1      0     0
25    0      1     0
26    1      0     0
30    0      1     0
33    1      0     0
35    1      0     0

The Solution: Specifying the Type Argument

The solution to this problem lies in specifying the type argument when calling the predict function. According to the rpart documentation, if type = “class”, then (for a classification tree) a factor of classifications based on the responses is returned.

# Make predictions using the predict function with type = "class"
pred_class <- predict(DecisionTree, test, type = "class")

# View the first 10 rows of the prediction matrix
head(pred_class, 10)

   fine tasty 
6     1    0 
14    1    0 
18    1    0 
23    1    0 
24    1    0 
25    0    1 
26    1    0 
30    0    1 
33    1    0 
35    1    0

Conclusion

In this article, we explored how to get numeric output from the predict function in rpart. By specifying type = “class” when calling the predict function, we can obtain a factor of classifications based on the responses, which is suitable for calculating accuracy. We also discussed the importance of understanding the rpart package and its documentation in order to achieve the desired output.

Additional Tips

Make sure to understand the different types of classification trees (e.g., logistic regression, probit model) and how they can be used for binary classification problems.
Familiarize yourself with the rpart package documentation and learn how to customize the decision tree using parameters such as cp (complexity parameter), method (method to use for pruning), and mincost (minimum cost of nodes).
Consider exploring other machine learning algorithms such as random forests, support vector machines, or neural networks for classification tasks.

Example Use Cases

Building a decision tree to predict the quality of wine based on various factors.
Classifying customers into different groups based on their demographic information and purchase behavior.
Predicting the outcome of a medical test based on the patient’s symptoms and test results.

Last modified on 2023-08-13