A Guide to Decision Tree Algorithm in Machine Learning

Summary: Learn how the Decision Tree algorithm in Machine Learning splits data for classification and regression tasks. This guide covers the algorithm’s working mechanism, including data splitting, recursive splitting, and stopping criteria. Understand the benefits of Decision Trees and the key algorithms involved, such as ID3, C4.5, and CART.

Introduction

One of the most popular algorithms in Machine Learning is Decision Trees, which are useful in regression and classification tasks. Decision trees are easy to understand and implement, making them ideal for beginners who want to explore the field of Machine Learning. 

The following blog is a guide to Decision Tree in Machine Learning, focusing on how it works and the need to use it in classification tasks.     

Read More: 

How Decision Trees Handle Missing Values: A Comprehensive Guide. 

Difference Between Underfitting and Overfitting in Machine Learning.   

What is a Decision Tree in Machine Learning?

Decision trees are Machine Learning algorithms that allow you to continuously split data based on a specific parameter in supervised learning. The Decision Tree algorithm in Machine Learning is explained by two entities: decision nodes and leaves. The decision nodes are where the data is split, and the leaves are the final decisions or outcomes.

For instance, Decision Tree Machine Learning can be evaluated using a binary tree given below. Accordingly, say you want to find out whether a person is physically fit based on the given information like age, height, weight, eating habits, etc. 

The decision nodes act as questions like ‘What’s the age?’, ‘Does he/she exercise?’, ‘Does he eat a lot of burgers?’, etc. On the other hand, decision leaves are the final outcomes present, like either ‘fit’ or ‘unfit. This was a binary classification problem, implying that it was a yes/no problem to be solved.

Significantly, there are two types of Decision Trees including:

• Classification Trees (Yes/No Types): The example above is the classification tree where the outcome was a variable based on ‘fit’ or ‘unfit’ categories. Hence, the Decision Tree variable is categorical.
• Regression Trees (Continuous Data Types): The decisions or outcomes in this case of a variable are mainly continuous, for instance, 123. Accordingly, regression trees have target variables, which take input for continuous variables rather than class labels in leaves. They are useful for explaining decisions, identifying possible outcomes, and predicting potential outcomes.

Must Read: Classification vs. Clustering. 

How does the Decision Tree Algorithm work?

Understanding how the Decision Tree algorithm works is essential for anyone delving into Machine Learning. Decision trees are powerful tools for both classification and regression tasks. They work by recursively splitting a dataset into subsets based on the most significant feature at each step. Let’s explore the step-by-step process of how the Decision Tree algorithm works.

Data Splitting

The Decision Tree algorithm begins by analysing the entire dataset to identify the feature that best separates the data into distinct classes or target values. This is done using a criterion such as information gain, Gini impurity, or mean squared error. 

The chosen feature and its corresponding threshold create the first split, dividing the dataset into two or more subsets.

Recursive Splitting

Next, the algorithm applies the same splitting criterion to each subset, dividing them based on the most significant feature within each subgroup. This recursive process continues, with the algorithm evaluating the remaining features at each node and choosing the best splits to maximise homogeneity within the resulting subsets.

Stopping Criteria

The recursive splitting continues until a stopping criterion is met. Common stopping criteria include reaching a maximum tree depth, having a minimum number of samples per leaf node, or achieving a split where the resulting subsets are pure (i.e., all elements belong to the same class). These criteria prevent the tree from growing too complex and overfitting the training data.

Leaf Nodes and Predictions

Once the splitting process is complete, the final nodes of the tree, known as leaf nodes, represent the outcome or prediction. Each leaf node corresponds to a class label in a classification tree, determined by the majority class within that node. In a regression tree, each leaf node represents a continuous value, typically the mean or median of the target values within that node.

Pruning the Tree

Decision trees often undergo a pruning process to improve the model’s generalizability. Pruning involves removing branches that are of little importance or contribute to overfitting. This can be done using techniques such as cost complexity pruning, which balances the tree’s complexity with its performance on the training data.

Why use Decision Tree Classification?

Decision tree classification can be effectively used to solve numerous classification problems. Some of the advantages of using Decision tree classification are as follows:

• Compared to other algorithms, Decision Trees require much less effort for data preparation during pre-processing
• A Decision Tree in Machine Learning does not require you to normalise data.
• Additionally, Decision Trees also do not require scaling of data
• Missing values within the data do not affect the process of building a Decision Tree to any considerable extent
• Furthermore, a Decision Tree model is highly intuitive and easy to explain to any technical team and stakeholders
• The simplicity of Decision Trees enables you to code, visualise, interpret and even manipulate simple Decision Trees. Even for beginners, Decision Tree classification is easy to understand and learn
• Moreover, Decision Trees follow a non-parametric method, implying that it’s distribution-free and doesn’t depend on probability distribution assumptions
• Decision trees tend to perform feature section or variable screening thoroughly. It can work on categorical and numerical data and handle problems with multiple outputs
• When using Decision Trees, non-linear relationships between parameters do not influence the performance of the trees, unlike other classification algorithms

What are the Algorithms used in the Decision Tree of Machine Learning?

As you know, Decision Trees stand out due to their simplicity and interpretability. However, their effectiveness largely depends on the algorithms used in the Decision Tree algorithm. These algorithms determine how the tree is built and how decisions are made at each node. Let’s delve into the critical algorithms used in the Decision Tree algorithm.

ID3 (Iterative Dichotomiser 3)

The ID3 algorithm is one of the earliest and most straightforward algorithms used in Decision Tree construction. It employs a top-down, greedy approach to split the dataset into subsets. The splitting criterion in ID3 is based on information gain, a measure of the reduction in entropy. 

By selecting the attribute that provides the highest information gain, ID3 ensures that the dataset is split to maximise the homogeneity of the target variable within the resulting subsets.

C4.5

Building upon ID3, the C4.5 algorithm introduces several enhancements. One significant improvement is the ability to handle both categorical and continuous attributes. C4.5 uses a metric called gain ratio, which adjusts information gain by considering the intrinsic information of a split. 

This helps in avoiding biases towards attributes with many distinct values. Additionally, C4.5 can handle missing values and prune the tree to prevent overfitting, making it a more robust and versatile algorithm.

CART (Classification and Regression Trees)

CART is another popular algorithm used in the Decision Tree algorithm. It supports both classification and regression tasks. For classification, CART uses the Gini impurity measure to select the best split, aiming to create pure subsets of data. 

For regression, it minimises the Mean Squared Error (MSE) to determine the optimal splits. CART also introduces binary splits, ensuring that each node divides the data into precisely two subsets, simplifying the tree structure and enhancing computational efficiency.

CHAID (Chi-squared Automatic Interaction Detector)

CHAID is a statistical algorithm used in Decision Tree construction, primarily for categorical data. It uses the chi-squared test to identify the best splits, ensuring each is statistically significant. 

CHAID can generate multi-way splits unlike other algorithms, leading to more complex but potentially more insightful trees. It is beneficial for exploratory data analysis and identifying interaction effects between variables.

Read More: 

Unlocking the Power of KNN Algorithm in Machine Learning.

Anomaly detection Machine Learning algorithms.

Frequently Asked Questions

What is a Decision Tree in Machine Learning?

A Decision Tree is a Machine Learning model used for classification and regression tasks. It works by splitting data into subsets based on specific criteria, forming a tree structure of decisions. Each internal node represents a decision rule, while each leaf node represents an outcome or prediction.

How does the Decision Tree Algorithm work?

The Decision Tree algorithm recursively splits data into subsets based on the most significant features at each step. It uses criteria like information gain, Gini impurity, or mean squared error to determine the best splits, ensuring maximum homogeneity within subsets and preventing overfitting.

Why use the Decision Tree Algorithm for classification?

The Decision Tree algorithm is ideal for classification because it is easy to understand and visualise. It requires minimal data preparation, handles both numerical and categorical data, and can effectively manage missing values. Its non-parametric nature allows it to model complex, non-linear relationships without assumptions about data distribution.

Conclusion

The above blog explains the concept and application of Decision Tree in Machine Learning in detail. Considering that classification and clustering are the most popular algorithms in Machine Learning, the differences lie in the pre-defined labels present in classification. 

Decision Trees are an important algorithm of Supervised Machine Learning that splits data based on pre-defined parameters continuously.