Decision tree learning algorithms are supervised machine learning algorithms that solve classification and regression problems. These models split up data through branches based on feature values until at the very end, a prediction is made; this setup closely aligns with human decision logic. Each internal node represents a decision based on a feature, whereas each branch represents results of that decision, and each leaf corresponds to a final prediction or class label. This intuitiveness makes them easily interpretable and graphical, hence their application in various fields.
Types of decision trees learning algorithms:
Decision tree algorithms are varied according to how splits are conceived, what types of data they handle, and how computationally efficient they are. ID3 is the basic algorithm which splits or bifurcates depending upon information gain and works well for classification, though it tends to overfit and exhibits problems with continuous attributes from the get-go. Based on ID3, C4.5 adds gain ratio for more effectively dealing with discrete and continuous data, though it can struggle in noisy environments. CART is a general-purpose algorithm applied to both classification and regression; it optimizes Gini impurity for classification and mean squared error (MSE) for regression, and includes pruning for diminishing overfitting. CHAID uses chi-square tests for split and is best suited for large categorical data, although it’s not best for continuous variables. CART is extended by Conditional Inference Trees use statistical hypothesis testing to perform unbiased splits with multiple types of data but are generally slower than standard tree algorithms because they have stringent testing mechanisms.
Decision tree learning algorithms examples:
Decision trees find their applications in real-world instances. They diagnose diseases based on the symptoms in the healthcare system. They assess loan eligibility by considering income and credit score in finance. They forecast a particular weather condition based on factors such as temperature and humidity in meteorology. They recommend products based on the analysis of user behavior in e-commerce. They are versatile due to their ability and flexibility to work with numerical as well as categorical data.
Top 10 decision tree learning algorithms:
- ID3 (Iterative Dichotomiser 3)
ID3 is one of the earliest classes of decision tree algorithms, developed by Ross Quinlan. It uses the information gain to select the best feature on which to split the data at each instance of a node. The algorithm calculates entropy that signifies the impurity of a dataset and selects the feature that gives the largest decrease in entropy. ID3 is a very simple and elegant approach to classification problems. However, it suffers when dealing with continuous data. Also, ID3 does not work well in the presence of noise or when the training data is very small, as it tends to overfit the data.
- C4.5
C4.5 is an extension of the ID3 algorithm and solves many of its shortcomings. Most importantly, it introduces the “gain ratio” as a splitting criterion, so that information gain is normalized and is not biased toward features with many values. It also includes support for continuous attributes, pruning, and handling missing values, ideal features to make it robust and applicable to real-life datasets. It is one of the most influential algorithms in decision tree learning.
- CART (Classification and Regression Trees)
CART is an all-purpose medium for the classification and regression. They evaluate Gini impurity or sometimes called error, while regression uses Mean Squares Errors (MSE) to quantify the accuracy of splits. CART always grows binary trees; that is, each node can split exactly into two branches. It uses cost-complexity pruning to improve accuracy and avoid overfitting and hence, is widely used in modern ML.
- CHAID (Chi-squared Automatic Interaction Detector)
The chi-square tests determine the best splits, so this is best for categorical data and multiway splits. Unlike CART, CHAID can create trees with more than two branches per node. It’s particularly effective in market research, survey analysis, and social science applications, where categorical variables dominate. However, it’s less effective with continuous data and may require discretization.
- QUEST (Quick, Unbiased, Efficient Statistical Tree)
QUEST uses statistical tests to produce an unbiased and quick decision tree splitting. It can avoid the bias that some algorithms yield regarding the variable with many levels and is efficient in handling large datasets. QUEST accepts explanatory variables, either categorical or continuous, and provides pruning mechanisms. It is rarely used in preference to CART or C4.5 but is appreciated for its statistical rigor and for speed.
- Random Forest
Random Forest is an ensemble learning method where many trees are constructed using bootstrap samples and random sampling of features, and then each tree votes for the final prediction. This leads to better accuracy and less overfitting. It works well for classification and regression problems and handles large data sets with higher dimensions. Being fast, robust, and scalable, Random Forest is often used as a benchmark in predictive modeling.
- XGBoost (Extreme Gradient Boosting)
XGBoost works by sequentially building trees, with each one focusing on correcting the errors of the previous one by regularizing to avoid overfitting, and it is generally optimized for speed and performance. XGBoost has become a go-to algorithm in data science competitions due to its high accuracy and efficiency. It supports parallel processing and handles missing values gracefully.
- LightGBM (Light Gradient Boosting Machine)
LightGBM stands for Light Gradient Boosting Machine and is a speed- and scale-oriented gradient boosting algorithm developed by Microsoft. Using a leaf-wise tree growth strategy, LightGBM basically results in deeper trees and better accuracy. It is helpful when working with large datasets and supports categorical features natively. It is widely used across industries for various applications like fraud detection, recommendation systems, and ranking problems.
- Extra Trees (Extremely Randomized Trees)
The execution of Extra Trees resembles that of Random Forest, but more randomness is inducted as splitting thresholds are chosen at random and not optimized. This increases bias and reduces variance and may lead to faster training times. If your dataset is prone to overfitting, this method may be useful, and it is beneficial when dealing with high-dimensional data. In ensemble learning, Extra Trees are often employed to increase generalization.
- HDDT (Hellinger Distance Decision Tree)
HDDT uses the Hellinger distance as a splitting criterion, making it effective for imbalanced datasets. It’s particularly useful in domains like fraud detection and rare event modeling, where traditional algorithms may falter.
