Are you wondering ‘What is a decision tree in R programming? Well, we are here to clear your doubts and concepts related to the topic. From the definition to the model, we will cover everything in this article.
In the domain of Machine Learning and Data Science, different algorithms play different roles. One such crucial algorithm is the decision tree algorithm which helps us to perform tasks like regression and classification.
As we move further in this article, we will see how these tasks can be handled with the help of decision trees. So, let us take a deep dive to learn all about decision trees.
Summary Of The Article:
Decision Trees are those models in Machine Learning that help us to perform tasks such as regression and classification.
Like binary trees in computer science, these also have a tree-like structure comprising of nodes and branches.
Decision trees are easy to interpret, and hence, are one of the most widely used algorithms in ML and Data Science.
Like any other algorithm, decision trees also have their advantages and disadvantages. We will also cover them in the later sections.
What Is A Decision Tree? Know The Basics
Decision trees are supervised algorithms in Machine Learning. Supervised algorithms are those algorithms that require human supervision or labeled datasets to perform tasks like classification and decision-making.
We can use a decision tree for both classification and regression tasks. These algorithms are highly versatile and easy to interpret, thus, making them one of the most used machine learning algorithms.
Machine learning is a special domain of computer science. If youโre interested in knowing the importance of Machine learning in programming languages like Python, then you can check out our article.
Let us see what is the structure of a decision tree model and what are basic components and terminologies associated with it.
What Is The Structure Of A Decision Tree?
As the name suggests, this algorithm has a tree-like structure with root nodes and branches stemming from each node or leaf. It is pretty much like a binary tree that you may have studied in data structures and algorithms.
They are the representation of all possible solutions or outcomes that can take place if a certain condition is met. It starts with an independent variable and then branches off to other parameters.
Below is the decision tree representation that can help you learn about its structure and components in a better way.
As we can see from the image above, the decision tree originated from a point called the root node. It then splits into two branches and keeps on repeating this process until a certain point.
A Decision Tree Consists Of Three Key Components –
Root Node: A root node shows us the independent variable. It is the starting point of a decision tree. The independent variable is also known as the best predictor.
Internal Node: The internal nodes are the nodes where the predictor variables or the independent variable is tested. Based on this test, the decision tree further branches out. It helps in making a decision, therefore, the internal nodes are also known as decision nodes.
Terminal Node: Terminal nodes or leaf nodes are the last nodes of the decision tree. These nodes hold the final classification value or labeled data.
Remember about these components of a decision tree. These will help us to build or model a decision tree in R programming in the later section of this article.
But before that, let us learn what techniques can we use in a decision tree. Let us dive in!
Different Techniques That We Can Use In A Decision Tree
Some techniques are used to create a model of a decision tree. These techniques show us how decision trees work. Let us have a look at them below.
Partitioning
Partitioning a decision tree means splitting its data into subsets. Using this process, we can split a node into sub-nodes. The process increases the overall accuracy and provides clarity of a node concerning the target variable.
Algorithms like Gini index and chi-square test are used for this purpose. The algorithm that gives us the best efficiency and accuracy is selected.
Pruning
Pruning is a process by which we can avoid the problem of overfitting in our model. Overfitting occurs when our model keeps on adding new nodes in the tree to fit all the data in it. This increases the complexity of our decision tree.
Using the pruning technique, we can shorten or reduce the size of our decision trees by turning the branch nodes into terminal nodes. As we make the tree model simple and shorter, our model significantly reduces the noise and anomalies as well.
Model Selection
Model selection is the process that helps us to choose the best decision tree for our model. Usually, the smallest tree is the most efficient tree model.
Keep in mind factors such as entropy and information gain when you select a model. These factors help us to gain information about the training data and the target variable for tasks like classification.
What Are The 2 Main Types Of Decision Trees?
Decision Trees are mainly of two types – classification and regression trees. Both of these models have a different purpose. While the classification tree works on categorical variables or data, the regression tree works on continuous variables.
Let us study both of these decision trees one by one. Keep reading below to know more!
Classification Tree
The classification trees mainly deal with labeled outcomes like ‘yes’ or ‘no.’ We can use such tree models for classification tasks and to predict a qualitative response.
Here, we predict which variables or observations belong to which class labels. For example, classifying whether a student passed or failed the exams based on their overall score and extra credits.
Gini Index, Entropy, and information gain are some classification tree-based algorithms that can be used to classify categorical data.
Regression Tree
Let us now know how are decision trees used for regression.
A regression tree is used to predict continuous values. Here, the target variables can have values from a wide range of data. We aim to predict a more quantitative response rather than a qualitative one.
You may have heard of the logistic regression algorithm in Machine Learning. We can use the algorithm in a regression tree.
How To Build A Decision Tree Model In R Programming?
You might be wondering, ‘How to make a classification tree in R?’ or ‘How to build a regression model?’ Let us implement this step-by-step using R programming.
Now, we will be learning about building models for decision trees in R with the help of the Titanic dataset available on Kaggle. Let us get started!
Data Preparation
Our first step is to acquire the data set and load it into R. For this, first, we will have to download the ‘train.csv’ file and use it later.
Install the necessary packages in your code. Here, we are using the part library. The next step would be to load the data.
The basic syntax for it will be: read.csv(“filename”)
# Install rpart package
install.packages("rpart")
install.packages("rpart.plot")
library(rpart)
library(rpart.plot)
# Loading the training dataset ---> make sure you replace the path with the actual path
data <- read.csv("train.csv")
head(data)
Note: Remember to replace the path in the above code with the actual path of the data set file you are using. Otherwise, an error will arise.
Let us see what our dataset looks like by data exploration to see what features and labels we have here.
From the above visual representation of our data set, we can see that we have a mix of categorical as well as numerical data present here.
Handling Missing Data
Data preprocessing is the process by which we can refine our data and handle missing values and noisy data. In this case, we will be performing it on our data to get a clean data set.
# handling missing values
# impute missing values with mean for numerical values
data$Age[is.na(data$Age)] <- mean(data$Age, na.rm = TRUE)
# encode categorical values
data$Sex <- factor(data$Sex, levels = c("male", "female"), labels = c(1, 2))
From the above code snippet, we can see that to handle any missing value in the age category, we substitute it with the mean response. We are also categorizing or labeling the gender into numerical values, 1 and 2. This will help us to keep our data set consistent for the model.
Additionally, you may also require feature scaling in your data set to further normalize your data.
Splitting Training Data
In this step, we will split the data set into the training and testing sets. This is necessary as it helps us to build and train the model and also test our model’s performance on new data or unseen data called as test data.
This is done based on some splitting rules. Here, we have used the set.seed() method to randomly generate some numbers for test data and training data.
# Split the data into training and testing sets
set.seed(123)
train_index <- sample(1:nrow(data), 0.7 * nrow(data))
train_data <- data[train_index, ]
test_data <- data[-train_index, ]
An alternate way to split our data set into the training set and a test set would be by splitting it into a 4:1 ratio. Most developers in data science use this ratio for data set splitting.
Building The R Decision Tree Model
Let us finally see how to build R decision tree models! Here, we will define a formula for the model to specify the target variable and the predictor variable and then use part() for the decision-making process.
# Building the model
decision_tree <- rpart(Survived ~ Age + Sex + Pclass, data = train_data,
method = "class", control = rpart.control(minsplit = 10, minbucket = 5))
In our case, we have selected fields(dependent variable) like age, sex, and Pclass for predictive modeling and to build the classification tree. Let us see the visual representation of the decision tree below.
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# for the visual representation of the tree
rpart.plot(decision_tree, main = "Decision Tree for Titanic Survival Prediction")
On running the above line of code, we will be able to see the visual representation of our Titanic survival prediction model based on the Titanic dataset we have selected.
From the image above, we can clearly see the root node, decision nodes, and terminal node as well that contain our survival labels or the predicted class(0 and 1)
Model Prediction And Evaluation
Now that we have trained our model on the training set, we will use the test set to evaluate the performance of our model. For this, we will use metrics like confusion matrix and accuracy test. Have a look at the code snippet below.
# Predict survival on the test set
predictions <- predict(decision_tree, test_data, type = "class")
# Create confusion matrix
conf_matrix <- table(predictions, test_data$Survived)
# Calculate accuracy
accuracy <- sum(diag(conf_matrix)) / sum(conf_matrix)
# Printing the confusion matrix and accuracy
print("Confusion Matrix:")
print(conf_matrix)
print(paste("Accuracy:", round(accuracy, 2)))
Let us look at the output to see what are the predictions and the accuracy of our tree algorithm.
So, how many passengers have survived? By making predictions, our algorithm has correctly classified that 140 passengers did not survive while 72 passengers survived with an accuracy of 0.79
Advantages And Disadvantages of Decision Tree Algorithms
Like Other Machine Learning algorithms or Data Science techniques, decision trees in R also have various pros and Cons. Let us discuss some of them below!
Advantages
They are easy to interpret and understand.
Decision trees can handle both continuous and categorical data.
Data preprocessing is comparatively simpler than other ML algorithms.
They can help to identify important variables for predictions.
Disadvantages
They require higher training time.
Decision trees can be less accurate than other algorithms in ML.
They are liable to overfitting.
The smallest change can affect the whole structure and working of the algorithm.
Conclusion:
Decision Tree In R programming language is one of the simplest algorithms in ML and Data Science that can help us understand our data and make informed decisions. We got to know when decision trees are most useful as well. They are versatile and can be used for both classification and regression.
I am sure that by now you have all the information you need to create your tree-based algorithms and deploy your ML models. If you find yourself stuck on a particular concept, then Codingzap’s highly skilled team of experts is always available to guide you through!
Takeaways:
Various algorithms in the domain of ML can help with tasks like classification or regression.
One such algorithm is the decision tree algorithm which can help us to interpret the data and make the decision for prediction easy.
This algorithm has a tree-like structure comprising nodes and branches that span out as new data is added.
At the end, the terminal node ( node at maximum depth) is present that stores the label of our predicted outcome.
While trees may be simple and easy to understand, the slightest change in the data may affect the whole algorithm.
