Decision trees serve as the basic building blocks for a family of machine-learning techniques known as tree-based models. Their interpretability, adaptability, and robust performance on a range of tasks, such as classification, regression, and ranking issues, have made them frequently employed. The main ideas, several kinds of tree-based models, their benefits, and a few real-world examples are all covered in this review.
Source: Tree-based models
Key Concepts in Tree-Based Models
Decision Trees
A decision tree is a structure resembling a flowchart, with internal nodes standing in for decisions based on criteria, branches for decisions’ results, and leaf nodes for decisions or final outputs.
Splitting Criteria: At each node, the data is split based on metrics such as variance reduction, entropy, and Gini impurity.
Ensemble Methods
To increase robustness and speed, combine several trees.
Bagging: Constructs many trees separately from various data subsets.
Boosting: Boosting is building trees one after the other, fixing each other’s mistakes.
Types of Tree-Based Models
Decision Trees
Models that are easy to understand and can be used for both regression and classification problems.
Random Forest
A group technique that uses bagging to create many decision trees then aggregates the trees’ forecasts to increase precision and decrease overfitting.
Gradient Boosting Machines (GBM)
Sequentially builds trees, to lower the residual errors of the trees built before it.
Source: Types of trees
Extreme Gradient Boosting (XGBoost)
A scalable, optimized gradient boosting solution with regularisation and other advanced features.
LightGBM
A distributed and effective gradient-boosting system based on tree-based learning methods.
CatBoost
An automated and effective gradient-boosting library for handling categorical features.
Advantages of Tree-Based Models
Interpretability
Decision trees help derive insights from the data since they are simple to comprehend and analyze.
Versatility
Able to do tasks related to ranking, regression, and classification using both numerical and categorical data.
Handling Non-Linear Relationships
Non-linear correlations between characteristics and the target variable can be captured via trees.
Robustness to Outliers
Compared to linear models, decision trees exhibit less sensitivity to outliers.
Feature Importance
A measure of feature significance is provided by tree-based models, which aid in feature selection and model behavior comprehension.
Challenges and Solutions
Overfitting
Deep decision trees in particular may overfit the training set. This is lessened by employing strategies like pruning, establishing a maximum depth, and ensemble approaches (such as Random Forests and Boosting).
Computational Complexity
It can be computationally costly to build deep trees or a large number of trees in an ensemble. The goal of optimized implementations such as LightGBM and XGBoost is to increase efficiency.
Bias in Data
Biases that exist in the training data can be propagated by tree-based models. It is essential to make sure the training set is impartial and representative.
Tree-based models, which combine interpretability, adaptability, and performance in a well-balanced manner, are a basic machine learning method. These models are effective for a variety of applications, whether they are used with basic decision trees or sophisticated ensemble techniques like Random Forests, Gradient Boosting, and optimized variations. It is vital to comprehend the advantages and drawbacks of tree-based models to utilize them efficiently in real-world scenarios.