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Gradient Boosting Models

By Moloy De posted Fri August 11, 2023 10:03 PM

  

Gradient boosting is a machine learning technique used in regression and classification tasks, among others. It gives a prediction model in the form of an ensemble of weak prediction models, which are typically decision trees. When a decision tree is the weak learner, the resulting algorithm is called gradient-boosted trees; it usually outperforms random forest. A gradient-boosted trees model is built in a stage-wise fashion as in other boosting methods, but it generalizes the other methods by allowing optimization of an arbitrary differentiable loss function.


Gradient tree boosting

Gradient boosting is typically used with decision trees, especially CARTs (Classification And Regression Trees) of a fixed size as base learners. For this special case, Friedman proposes a modification to gradient boosting method which improves the quality of fit of each base learner.




Usage

Gradient boosting can be used in the field of learning to rank. The commercial web search engines Yahoo and Yandex use variants of gradient boosting in their machine-learned ranking engines. Gradient boosting is also utilized in High Energy Physics in data analysis. At the Large Hadron Collider (LHC), variants of gradient boosting Deep Neural Networks (DNN) were successful in reproducing the results of non-machine learning methods of analysis on datasets used to discover the Higgs boson. Gradient boosting decision tree was also applied in earth and geological studies – for example quality evaluation of sandstone reservoir.


Disadvantages

While boosting can increase the accuracy of a base learner, such as a decision tree or linear regression, it sacrifices intelligibility and interpretability. For example, following the path that a decision tree takes to make its decision is trivial and self-explained, but following the paths of hundreds or thousands of trees is much harder. To achieve both performance and interpretability, some model compression techniques allow transforming an XGBoost into a single "born-again" decision tree that approximates the same decision function. Furthermore, its implementation may be more difficult due to the higher computational demand.

QUESTION I: What is the reason behind Gradient Boosting Trees outperforming Random Forest?
QUESTION II: How to would be an ensembler on regression models?

REFERENCE : Gradient Boosting Wikipedia

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Comments

Olakunle Olaniyi
Tue April 09, 2024 09:05 PM

it's important to note that the performance of GBT and RF can vary depending on the dataset and the specific problem at hand. In some cases, RF may outperform GBT, especially when dealing with noisy data or when computational efficiency is a concern. Therefore, it's often recommended to experiment with both algorithms and select the one that performs better on a particular task.

Below is a detailed explanation of some of their differences

Gradient Boosting Trees (GBT) and Random Forest (RF) are both ensemble learning methods that combine multiple decision trees to make predictions. While both methods are powerful and widely used in machine learning, they have different strengths and weaknesses.

  1. Gradient Boosting Trees (GBT):

    • GBT is an iterative ensemble method where trees are built sequentially, with each new tree attempting to correct the errors made by the previous ones.
    • Trees in GBT are typically shallow, which means they have fewer nodes and thus tend to be less complex. This can help reduce overfitting.
    • GBT optimizes a differentiable loss function, often using gradient descent techniques. By minimizing this loss function, GBT can improve prediction accuracy.
    • GBT can capture complex relationships between features and the target variable by focusing on the errors made by previous trees, leading to high predictive accuracy.

  2. Random Forest (RF):

    • RF builds multiple decision trees independently and combines their predictions through averaging or voting.
    • Trees in RF are typically deep and can be more complex. Each tree is built independently of the others, which can lead to overfitting, especially with noisy data.
    • RF introduces randomness in the tree-building process by using random subsets of features and random subsets of the training data. This randomness helps reduce overfitting and improves generalization.
    • RF is robust and less prone to overfitting, especially when dealing with high-dimensional data or data with many noisy features.

Reasons why Gradient Boosting Trees might outperform Random Forest:

  1. Model Complexity: GBT tends to use simpler trees compared to RF, which can make them more interpretable and less prone to overfitting, especially on smaller datasets.

  2. Gradient Descent Optimization: GBT optimizes a loss function using gradient descent, which allows it to efficiently minimize errors and find the optimal solution.

  3. Sequential Learning: GBT builds trees sequentially, learning from the mistakes of previous trees. This sequential learning process can lead to better performance, especially when dealing with complex relationships in the data.

  4. Handling Class Imbalance: GBT can handle class imbalance better than RF by focusing more on the misclassified instances, which can lead to improved performance, especially in classification tasks with imbalanced classes.