Gradient Boosting in Machine Learning

Why do we need Boosting?

Before learning gradient boosting technique lets understand the need for boosting with the help of a scenario. Suppose, we have separately built six Machine Learning models for predicting whether it will rain or not. Each of these models has been built on top of the 6 distinct parameters given below to analyze and predict the weather condition:

1. Air temperature
2. Atmospheric (barometric) pressure
3. Humidity
4. Precipitation
5. Solar radiation
6. Wind

The outputs from the Machine Learning models may differ for these six parameters. The model which is evaluating air temperature may predict a sunny day. Whereas, another model may predict a rainy day based on humidity. Also, even if we predict the outcome on the basis of a single model, then there is a 50% probability of false prediction. These individual models are weak learners. But, if we combine all the weak learners to work as one, then the prediction would rely on 6 different parameters. The increase in the number of parameters will boost the accuracy of the model.

This is the logic behind all the boosting techniques such as AdaBoost, gradient boosting, and XGBoost.

Now, in this blog on ‘Gradient Boosting,’ we will understand ‘What is Boosting?’

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What is Boosting?

Boosting is a technique to combine weak learners and convert them into strong ones with the help of Machine Learning algorithms. It uses ensemble learning to boost the accuracy of a model. Ensemble learning is a technique to improve the accuracy of Machine Learning models. There are two types of ensemble learning:

1. Sequential Ensemble Learning

It is a boosting technique where the outputs from individual weak learners associate sequentially during the training phase. The performance of the model is boosted by assigning higher weights to the samples that are incorrectly classified. AdaBoost algorithm is an example of sequential learning that we will learn later in this blog.

2. Parallel Ensemble Learning

It is a bagging technique where the outputs from the weak learners are generated parallelly. It reduces errors by averaging the outputs from all weak learners. The random forest algorithm is an example of parallel ensemble learning.

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Mechanism of Boosting Algorithms

Boosting is creating a generic algorithm by considering the prediction of the majority of weak learners. It helps in increasing the prediction power of the Machine Learning model. This is done by training a series of weak models.

Below are the steps that show the mechanism of the boosting algorithm:

1. Reading data

2. Assigning weights to observations

3. Identification of misinterpretation (false prediction)

4. Assigning the false prediction, along with a higher weightage, to the next learner

5. Finally, iterating Step 2 until we get the correctly classified output

Now, we will explore various interpretations of weakness and their corresponding algorithms.

Types of Boosting Algorithms

Basically, there are three types of boosting algorithms discussed as below:

1. Adaptive Boosting (AdaBoost)

Adaptive boosting is a technique used for binary classification. For implementing AdaBoost, we use short decision trees as weak learners.

Steps for implementing AdaBoost:

1. Train the base model using the weighted training data

2. Then, add weak learners sequentially to make it a strong learner

3. Each weak learner consists of a decision tree; analyze the output of each decision tree and assign higher weights to the misclassified results. This gives more significance to the prediction with higher weights.

4. Continue the process until the model becomes capable of predicting the accurate result

Next, we will see “What is Gradient Boosting?.”

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2. Gradient Boosting

In Machine Learning, we use gradient boosting to solve classification and regression problems. It is a sequential ensemble learning technique where the performance of the model improves over iterations. This method creates the model in a stage-wise fashion. It infers the model by enabling the optimization of an absolute differentiable loss function. As we add each weak learner, a new model is created that gives a more precise estimation of the response variable.

The gradient boosting algorithm requires the below components to function:

1. Loss function: To reduce errors in prediction, we need to optimize the loss function. Unlike in AdaBoost, the incorrect result is not given a higher weightage in gradient boosting. It tries to reduce the loss function by averaging the outputs from weak learners.

2. Weak learner: In gradient boosting, we require weak learners to make predictions. To get real values as output, we use regression trees. To get the most suitable split point, we create trees in a greedy manner, due to this the model overfits the dataset.

3. Additive model: In gradient boosting, we try to reduce the loss by adding decision trees. Also, we can minimize the error rate by cutting down the parameters. So, in this case, we design the model in such a way that the addition of a tree does not change the existing tree.

Finally, we update the weights to minimize the error that is being calculated.

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How is this useful?

Gradient boosting is a highly robust technique for developing predictive models. It applies to several risk functions and optimizes the accuracy of the model’s prediction. It also resolves multicollinearity problems where the correlations among the predictor variables are high.

Gradient boosting machines have been successful in various applications of Machine Learning.

Next, we will move on to XGBoost, which is another boosting technique widely used in the field of Machine Learning.

3. XGBoost

XGBoost algorithm is an extended version of the gradient boosting algorithm. It is basically designed to enhance the performance and speed of a Machine Learning model.

Additionally, we have an XGBoosting library, which gives us Machine Learning frameworks of gradient boosting for various languages such as R, Python, Java, etc.

Why do we use XGBoost?

In the gradient boosting algorithm, there is a sequential computation of data. Due to this, we get the output at a slower rate. This is where we use the XGBoost algorithm. It increases the model’s performance by performing parallel computations on decision trees.

What features make XGBoost unique?

XGBoost is much faster than the gradient boosting algorithm. It improves and enhances the execution process of the gradient boosting algorithm.

There are more features that make XGBoost algorithm unique and they are:

1. Fast: The execution speed of the XGBoost algorithm is high. We get a fast and efficient output due to its parallel computation.

2. Cache optimization: To manage and utilize resources, it uses cache optimization.

3. Distributed computing: If we are employing large datasets for training the Machine Learning model, then XGBoost provides us distributed computing, which helps combine multiple machines to enhance performance.

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Implementation of Gradient Boosting

In this section, we will look into the implementation of the gradient boosting algorithm. For this, we will use the Titanic dataset.

Here are the steps of implementation:

1. Importing the required libraries

2. Loading the dataset

3. Performing data preprocessing

4. Concatenating a new dataset

5. Dropping the columns that are not required

6. Assigning empty sets a value of 0

7. Splitting the data into train and test sets

8. Scaling the data using MinMaxScaler

9. Selecting the size of the dataset for testing

10. Assigning the learning rate to evaluate the classifier’s performance

Performance of different learning rates:

11. Creating a new gradient boosting classifier and building a confusion matrix for checking accuracy

Output:

In this blog, we saw ‘What is Gradient Boosting?,’ AdaBoost, XGBoost, and the techniques used for building gradient boosting machines. Also, we implemented the boosting classifier and compared the accuracy of the model for different learning rates. This is all about how the gradient boosting technique incredibly enhances the performance of the Machine Learning models.

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