Introduction to Boosting Algorithms in Machine Learning

Lots of analysts misinterpret the term ‘boosting’ used in data science. Let me provide an interesting explanation of this term. Boosting grants power to machine learning models to improve their prediction accuracy. Boosting algorithms are one of the most widely used algorithms in data science competitions. The winners of our last hackathons agree that they try boosting algorithms to improve the accuracy of their models.

In this article, I will explain how boosting algorithms in machine learning work very simply. I’ve also shared the Python codes below. I’ve skipped the intimidating mathematical derivations used in Boosting because that wouldn’t have allowed me to explain this concept in simple terms. Also, we have talked about how it works and why boosting algorithms in machine learning are important.

Let’s get started.

What is Boosting?

The term ‘Boosting’ refers to a family of algorithms which converts weak learner to strong learners.

Let’s understand this definition in detail by solving a problem of spam email identification:

How would you classify an email as SPAM or not? Like everyone else, our initial approach would be to identify ‘spam’ and ‘not spam’ emails using following criteria. If:

1. Email has only one image file (promotional image), It’s a SPAM
2. Email has only link(s), It’s a SPAM
3. Email body consist of sentence like “You won a prize money of $ xxxxxx”, It’s a SPAM
4. Email from our official domain “Analyticsvidhya.com” , Not a SPAM
5. Email from known source, Not a SPAM

Above, we’ve defined multiple rules to classify an email into ‘spam’ or ‘not spam’. But, do you think these rules individually are strong enough to classify an email successfully? No.

Individually, these rules are not powerful enough to classify an email into ‘spam’ or ‘not spam’. Therefore, these rules are called as weak learner.

To convert weak learner to strong learner, we’ll combine the prediction of each weak learner using methods like:

• Using average/ weighted average
• Considering prediction has higher vote

For example:  Above, we have defined 5 weak learners. Out of these 5, 3 are voted as ‘SPAM’ and 2 are voted as ‘Not a SPAM’. In this case, by default, we’ll consider an email as SPAM because we have higher(3) vote for ‘SPAM’.

How Does Boosting Algorithms Work?

Now we know that, boosting combines weak learner a.k.a. base learner to form a strong rule. An immediate question which should pop in your mind is, ‘How boosting identify weak rules?‘

To find weak rule, we apply base learning or ML algorithms with a different distribution. Each time base learning algorithm is applied, it generates a new weak prediction rule. This is an iterative process. After many iterations, the boosting algorithm in machine learning combines these weak rules into a single strong prediction rule.

Here’s another question which might haunt you, ‘How do we choose different distribution for each round?’

For choosing the right distribution, here are the following steps:

• Step 1: The base learner takes all the distributions and assign equal weight or attention to each observation.
• Step 2: If there is any prediction error caused by first base learning algorithm, then we pay higher attention to observations having prediction error. Then, we apply the next base learning algorithm.
• Step 3: Iterate Step 2 till the limit of base learning algorithm is reached or higher accuracy is achieved.

Finally, it combines the outputs from weak learner and creates  a strong learner which eventually improves the prediction power of the model. Boosting pays higher focus on examples which are mis-classified or have higher errors by preceding weak rules.

Types of Boosting Algorithms

Underlying engine used for boosting algorithms can be anything.  It can be a decision stamp, margin-maximizing classification algorithm, etc. Many boosting algorithms in machine learning use other types of engines such as:

1. AdaBoost (Adaptive Boosting)
2. Gradient Tree Boosting
3. XGBoost

In this article, we will focus on AdaBoost and Gradient Boosting followed by their respective python codes and will focus on XGboost in upcoming article.

AdaBoost

This diagram aptly explains Ada-boost. Let’s understand it closely:

Box 1: You can see that we have assigned equal weights to each data point and applied a decision stump to classify them as + (plus) or – (minus). The decision stump (D1) has generated vertical line at left side to classify the data points. We see that, this vertical line has incorrectly predicted three + (plus) as – (minus). In such case, we’ll assign higher weights to these three + (plus) and apply another decision stump.

Box 2: Here, you can see that the size of three incorrectly predicted + (plus) is bigger as compared to rest of the data points. In this case, the second decision stump (D2) will try to predict them correctly. Now, a vertical line (D2) at right side of this box has classified three mis-classified + (plus) correctly. But again, it has caused mis-classification errors. This time with three -(minus). Again, we will assign higher weight to three – (minus) and apply another decision stump.

Box 3: Here, three – (minus) are given higher weights. A decision stump (D3) is applied to predict these mis-classified observation correctly. This time a horizontal line is generated to classify + (plus) and – (minus) based on higher weight of mis-classified observation.

Box 4: Here, we have combined D1, D2 and D3 to form a strong prediction having complex rule as compared to individual weak learner. You can see that this algorithm has classified these observation quite well as compared to any of individual weak learner.

How Does it Work?

AdaBoost works using a method similar to the one discussed above. It fits a sequence of weak learners on different weighted training data. It starts by predicting the original data set and assigning equal weight to each observation. If the prediction using the first learner is incorrect, then it assigns higher weight to the observation that was predicted incorrectly. Being an iterative process, it continues to add learner(s) until a limit is reached in the number of models or accuracy.

Mostly, we use decision stamps with AdaBoost. But, we can use any machine learning algorithms as base learner if it accepts weight on training data set. We can use AdaBoost algorithms for both classification and regression problem.

Checkout this Complete Guide for Classification in Machine Learning

Python Code

Here is a live coding window to get you started. You can run the codes and get the output in this window itself:

# Importing dependencies numpy and keras
import pandas as pd
from sklearn.ensemble import AdaBoostClassifier #For Classification
from sklearn.ensemble import AdaBoostRegressor #For Regression
from sklearn.tree import DecisionTreeClassifier

# reading the data
df = pd.read_csv('train-data.csv')

# first five rows of the data
print(df.head())

# shape of the data
print('\nShape : ', df.shape)

# separating the independent and dependent variables
# independent variable
x_train = df.drop('Survived', axis=1)
# dependent variable
y_train = df['Survived']

# Now we will use decision tree as a base estimator, you can use any ML learner as base estimator if it accepts sample weight 
dt = DecisionTreeClassifier() 
clf = AdaBoostClassifier(n_estimators=100, base_estimator=dt,learning_rate=1)

# training the model
clf.fit(x_train,y_train)

Furthermore, you can tune the parameters to optimize the performance of algorithms, I’ve mentioned below the key parameters for tuning:

• n_estimators: It controls the number of weak learners.
• learning_rate:Controls the contribution of weak learners in the final combination. There is a trade-off between learning_rate and n_estimators.
• base_estimators: It helps to specify different ML algorithm.

You can also tune the parameters of base learners to optimize its performance.

Gradient Boosting

In gradient boosting, it trains many model sequentially. Each new model gradually minimizes the loss function (y = ax + b + e, e needs special attention as it is an error term) of the whole system using Gradient Descent method. The learning procedure consecutively fit new models to provide a more accurate estimate of the response variable.

The principle idea behind this algorithm is to construct new base learners that can be maximally correlated with the negative gradient of the loss function and associated with the whole ensemble.  

In Python Sklearn library, we use Gradient Tree Boosting or GBRT. It is a generalization of boosting to arbitrary differentiable loss functions. It can be used for both regression and classification problems.

Python Code

from sklearn.ensemble import GradientBoostingClassifier #For Classification
from sklearn.ensemble import GradientBoostingRegressor #For Regression

clf = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0, max_depth=1)
clf.fit(X_train, y_train)

• n_estimators: It controls the number of weak learners.
• learning_rate:Controls the contribution of weak learners in the final combination. There is a trade-off between learning_rate and n_estimators.
• max_depth: maximum depth of the individual regression estimators. The maximum depth limits the number of nodes in the tree. Tune this parameter for best performance; the best value depends on the interaction of the input variables.

You can tune loss function for better performance.

Advantages of Boosting Algorithm

Several advantages that make boosting algorithm a powerful tool in machine learning:

• Improved Accuracy: By combining multiple weak learners into a single strong learner, boosting can significantly improve the overall accuracy of the model’s predictions.
• Reduced Bias: Boosting iteratively trains models to focus on areas where previous models struggled, reducing the overall bias of the final model.
• Handling Imbalanced Data: When dealing with datasets where some classes have significantly fewer examples than others, boosting algorithms in machine learning can be effective by assigning higher weights to the under-represented classes during training.
• Interpretability: While some ensemble methods can be like black boxes, boosting algorithms often provide a more interpretable view of the decision-making process by combining simpler models.
• Reduced Overfitting: Boosting’s sequential approach helps to mitigate overfitting, a common problem where a model performs well on training data but poorly on unseen data

Boosting Algorithm in Machine learning

Boosting is a  technique in machine learning that improves the performance of models by combining multiple weak learners into a single strong learner. Here’s a breakdown of how it works:

The Idea Behind Boosting: Imagine many average students (weak learners) working on the same problem. Each student might only get a few answers right. But if you combine all their answers smartly, you can end up with a much better understanding of the problem (strong learner). This is the core idea behind boosting algorithms in machine learning.

Conclusion

In this article, we looked at boosting, one of the methods of ensemble modeling, to enhance prediction power. We discussed the science behind the boosting algorithm in machine learning and its two types: AdaBoost and Gradient Boost. We also studied their respective Python codes.

Hope you clear the understanding about the concept of boosting in machine learning. Boosting techniques in machine learning, such as AdaBoost and Gradient Boosting, help improve model accuracy by combining weaker models into a stronger one. This approach focuses on correcting errors from previous models, leading to better overall performance.

Boost your machine learning expertise with our ‘Boosting Algorithms‘ course! Discover how boosting enhances prediction accuracy, explore AdaBoost and Gradient Boost, and implement them in Python—enroll now to elevate your skills and tackle complex challenges with confidence!

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Sunil Ray

Sunil Ray is Chief Content Officer at Analytics Vidhya, India's largest Analytics community. I am deeply passionate about understanding and explaining concepts from first principles. In my current role, I am responsible for creating top notch content for Analytics Vidhya including its courses, conferences, blogs and Competitions.

I thrive in fast paced environment and love building and scaling products which unleash huge value for customers using data and technology. Over the last 6 years, I have built the content team and created multiple data products at Analytics Vidhya.

Prior to Analytics Vidhya, I have 7+ years of experience working with several insurance companies like Max Life, Max Bupa, Birla Sun Life & Aviva Life Insurance in different data roles.

Industry exposure: Insurance, and EdTech

Major capabilities: Content Development, Product Management, Analytics, Growth Strategy.