Understanding the Gradient Boosting Algorithm

In the last articles, we discussed the introduction to boosting algorithms, and we got to know about the AdaBoost Algorithm. This article aims to continue this series of articles and discuss the Gradient Boosting algorithm.

This algorithm is a crucial part of the ensemble machine learning model’s family. The basic idea behind this algorithm is based on the main idea of boosting algorithm. As the name suggests, this algorithm is also based on the idea of the gradient descent algorithm.

As we already know that we use boosting algorithms to increase the accuracy of the machine learning modelling procedure. Here, the speed of making predictions and accuracy makes this algorithm stand out. This is the reason why we find its application in many competitions and real-life data science projects. Let’s start understanding the gradient boosting algorithm using the following table of contents.

Table of Contents

• What is the Gradient Boosting Algorithm?
• How Does The Gradient Boosting Work?
• Code Implementation
• Advantages and Disadvantages of Gradient Boosting

What is the Gradient Boosting Algorithm

Because of earlier articles, we already know the idea behind the boosting algorithm is to align multiple models in a sequence so that they can learn from the errors of the other models and, at the final stages, provide higher accuracy. One question which arises here is how does the gradient descent method work in this sequence alignment?

If we look a the gradient descent method, we find that it is an iterative optimization algorithm used to minimize a given loss function or cost function. In machine learning, we use it to update the parameters of a model or find the optimal values that minimize the difference between predicted and actual values.

When we make it work in boosting algorithm paradigm, we assign this method a role to optimize the parameters of the new model. In each iteration, the new model is trained to minimize the loss function by adjusting its parameters using gradient descent. Because of the use of this method in optimising and reducing the loss, we call this method gradient boosting method.

How Does The Gradient Boosting Work?

In the above, we get to know that because of using the gradient descent method to optimize the parameters of the new model in boosting, we call it gradient boosting. The key idea in gradient boosting is that each new model added to the ensemble focuses on correcting the mistakes made by the previous models. By iteratively minimizing the residuals and improving the predictions, the ensemble gradually increases its accuracy.

Let’s try and understand the working of this boosting algorithm using an example of a dataset with one input feature (x) and a continuous target variable (y). Here our goal is to build a regression model using the gradient boosting algorithm.

The following step this algorithm will follow:

1. It will start with training a base model(usually a decision tree). This model gives predictions for the entire dataset.
2. After making predictions, using the original values, it calculates the errors. These errors can be thought of as the parts of y that are still unexplained by the base model.
3. Before applying the next sequenced model, the algorithm applies the gradient descent method to calculate the optimized parameters of the next model.
4. Train a new model, and This model is fitted to minimize the difference between the predicted residuals and the actual residuals.
5. After training a new model, this algorithm calculates the new residuals by subtracting the updated predictions from the actual values of y. These residuals represent the remaining unexplained variability in the target variable.
6. This whole process gets repeated iteratively, adding the new models to the ensemble while applying gradient descent on it and updating the predictions and residuals.
7. At the final stage, the method combines the predictions from all the models where we consider the final predictions refined estimation of y.

The above example represents the regression model using the gradient boosting algorithm, and we can also perform classification using the same method. Now let’s take a look at the implementation of a gradient boosting algorithm using the Python programming language.

Code Implementation

To implement the gradient boosting algorithm in Python programming language, the Sklearn library gives the functionality under its ensemble learning package. In this article, we are using the popular breast cancer dataset(from Sklearn) to see the implementation. Let’s start by importing important libraries.

from sklearn.datasets import load_breast_cancer
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score,confusion_matrix

Now lets import the data.

data = load_breast_cancer()
X = data.data
y = data.target

Here we have imported the data, lets check the description of this data.

print('name of the Features \n', data.feature_names)
print('name of the classes \n', data.target_names)
print('name of the classes \n', data.data.shape)

Output:

Here we can see the name of the features, classes and size of the data. Lakers split the data so that after training the gradient boosting model, we can test the model and see its performance.

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Let’s train the gradient boosting model.

Defining model object

GBM = GradientBoostingClassifier(n_estimators=100, random_state=42)

Training the model

GBM.fit(X_train, y_train)

Making predictions from trained data.

y_pred = GBM.predict(X_test)

Lets validate the model by calculating the accuracy based on predictions made by model.

accuracy = accuracy_score(y_test, y_pred)
print("Accuracy of Gradient Boosting:", accuracy)

Output:

Here we can see the accuracy of the model, lets check for the confusion matrix to validate it more.

import pandas as pd
import numpy as np
cm = confusion_matrix(y_test, y_pred)
cm_df = pd.DataFrame(cm, index=[i for i in range(2)], columns=[i for i in range(2)])
cm_df

Output:

Here we can see that the model has made 5 wrong predictions out of 114 data points. This can be considered as a good performance of the model. Let’s take a look at the advantages and disadvantages of gradient boosting algorithm.

Advantages and Disadvantages of Gradient Boosting

Just like the other machine learning algorithms, this algorithm also has its own advantages and disadvantages. When we talk about the predictive power of different models, this algorithm has high predictive accuracy and is capable of capturing complex relationships and patterns in the data. There are several such advantages of it. Let’s take a look at them:

• It is capable of performing well in both classification and regression problems, so we can consider it as a flexible algorithm.
• Gradient boosting algorithms with decision trees can handle missing values in the data by using surrogate splits during the tree construction process.
• Gradient boosting is relatively robust to outliers, as the influence of individual data points is mitigated through the ensemble approach.

Disadvantages of Gradient Boosting:

• While it is robust to outliers, it is also computationally expensive, especially when dealing with large datasets or deep trees. Training the model may require more time and resources compared to simpler algorithms.
• In the case of complex models or high learning rates, gradient descent can be prone to the overfitting problem.
• Gradient descent is often considered a less interpretable or black box model. Understanding the exact relationship between predictors and outcomes can be challenging due to the complex ensemble structure.

Final Words

In this article, we have seen how two different methods can be used together and create a new method. The above-discussed ensemble learning method is a combination of boosting and gradient descent algorithms. In this combination, boosting tries to align models in a sequence, whereas gradient descent tries to optimize the parameters of the new models in the sequence so that there can be more reduction in errors.

We can also think of this method as the first boosting algorithm which works with another machine learning algorithm. There are several advantages as well as disadvantages of this machine learning algorithm, and by knowing them, we can use this algorithm more appropriately.

About Us

Data Science Wizards (DSW) is a pioneering AI innovation company that is revolutionizing industries with its cutting-edge UnifyAI platform. Our mission is to empower enterprises by enabling them to build their AI-powered value chain use cases and seamlessly transition from experimentation to production with trust and scale.

At DSW, we understand the transformative potential of artificial intelligence and its ability to reshape businesses across various sectors. Through our UnifyAI platform, we provide organizations in the insurance, retail, and banking sectors with a unique advantage by offering pre-learned use cases tailored to their specific needs.

Our goal is to drive innovation and create tangible value for our clients. We believe that AI should not be limited to a theoretical concept but should be harnessed as a practical tool to unlock business potential. By leveraging the power of UnifyAI, enterprises can accelerate their AI initiatives, achieve operational excellence, and gain a competitive edge in the market.

We prioritize trust and scalability in everything we do. We understand the importance of reliable and secure AI solutions, and we strive to build systems that can be seamlessly integrated into existing workflows. Our platform is designed to facilitate the transition from experimental AI projects to large-scale production deployments, ensuring that our clients can trust in the stability and scalability of their AI-powered solutions.

To learn more about DSW and our ground-breaking UnifyAI platform, visit our website at www.datasciencewizards.ai Join us in shaping the future of AI and transforming industries through innovation, reliability, and scalability.