Boosting Algorithms vs. Random Forests Explained

Imagine yourself in the position of a marketing analyst for an e-commerce site who has to make a model that will predict if a customer purchases in the next month or not. In such a scenario, you might have to work with huge datasets, possibly the information of tens of thousands of customers, with each record having over 50 features, such as the customers’ ages, past purchases, buying behavior, etc. 

Now, when it comes to tackling this challenge, you have a range of approaches at your disposal. One avenue worth exploring is the utilization of boosting algorithms like LightGBM or XGBoost. Or maybe you could go down the path of using random forests, which is another great option. However, how do you make the final call? Well, don’t worry, this is exactly what we’re going to be looking at today.

In today’s article, I will be explaining how all of these methods are different from one another and which one would be the best option for you, depending upon your specific circumstances. So, let’s get going!

Here’s a quick take-home table for you to remember the key differences between these algorithms to know when to use which one.

	AdaBoost	XGBoost	LightGBM	Random Forest
When to Use	When dealing with weak learners, noisy data, and imbalanced datasets	When working with large-scale, high-dimensional, and complex datasets	When dealing with time-sensitive tasks or when memory is concerned	When working with high-dimensional data and for classification and regression tasks

Understanding Boosting Algorithms – What Are Their Strengths and Weaknesses?

Essentially, boosting is an ensemble learning algorithm that creates a strong learner based on multiple weak learners. What’s happening in the background is that it sequentially trains every learner independently where every subsequent learner tries to correct the errors that were made by the previous learner. All of this is done by assigning more weight to the mistakes of the previous learner so the next one knows what to avoid.  

Let’s go over the strengths and weaknesses of some popular boosting algorithms: 

1. AdaBoost 

Ada (Adaptive Boost) is a machine learning algorithm that is mainly used for binary classification problems e.g., checking whether an email is spam or not. This algorithm trains weak classifiers (decision trees or “stumps”) and turns them into strong classifiers by assigning more weight to classifiers that perform better. In the end, every classifier’s results are combined based on the weights assigned to make a new strong learner. 

Strengths: 

• Versatile: Can work with many kinds of weak learners such as SVMs or neural networks 
• Robust: Since it focuses more on the difficult instances it is more robust and immune to overfitting. 
• Feature Selection: The algorithm assigns a higher weight to misclassified features which allows it to focus more on relevant features and generalize better. 

Weaknesses: 

• Sensitivity to Noise: If there is a lot of noise in the dataset or too many outliers in the training dataset then the algorithm’s performance is compromised. 
• Expensive: The sequential training of every classifier makes it slow and expensive to compute. 
• Lack of Transparency: The final model can be very complex, and it becomes difficult to understand the contribution of every individual learner. 
• Biased: If the datasets that you provide are imbalanced then the algorithms may focus on the majority class instead of the minority class. 

2. XGBoost 

XG (Extreme Gradient) Boost is another type of boosting algorithm that is mainly used for supervised learning tasks e.g., training a model for image classification purposes. The main algorithm behind XGBoost remains the same i.e., iteratively training weak learners to make a strong learner by correcting the mistakes of its predecessors.  

What’s different here is that before the training process, you initialize the models with your initial predictions. It also makes a decision tree based on the mistakes made by the previous learners and the important features. Additionally, it trains every model by incorporating Regularization (L1 and L2 regularization) and Gradient Decent Optimization to control the complexity and minimize the loss function. After every few iterations, the model is tested using a validation set to see if there is improvement in the results and if there isn’t, it stops. 

Strengths: 

• Accurate and Scalable: It has mainly gained popularity for its performance, high accuracy, and its ability to efficiently handle large datasets with its parallel processing capabilities. 
• Regularization: It incorporates regularization techniques in the algorithm to prevent it from overfitting and improve its generalization abilities. 
• Flexible: It can easily support various objective functions and evaluation metrics which allows you to customize it according to the problem’s requirements. 
• Feature Importance: The insights it provides about the feature insights let you understand the role of every feature in the model’s predictions. 

Weaknesses: 

• Complex: To obtain optimal results you must carefully tune the hyperparameters which not only makes it too complex to compute but also makes the final model difficult to interpret. 
• Sensitivity to Hyperparameters: Since the results majorly depend on the tuning of the hyperparameters, it makes the model very sensitive to the hyperparameters.  
• Imbalanced Data Handling: Although XGBoost can handle imbalanced datasets that require too much work, i.e., adjusting class weights or oversampling. 
• Storage Issues: Relative to other algorithms, this one takes up the most storage especially when you’re working with a very large dataset. 

3. LightGBM 

LightGBM is a gradient-boosting framework that handles both classification and regression tasks e.g., sentiment analysis or predicting prices. This algorithm is always a good option when you have to work on large-scale machine-learning tasks.  

The first step when working with this algorithm is that it must first be arranged in a tabular form and the model should be initialized with the initial prediction. After that, it makes the decision trees or “boosting rounds” and iteratively tests them and combines every tree’s result to produce the final learner. 

This algorithm also incorporates gradient-based optimization to calculate the loss function of each learner. To speed up the process as well as use less space, it takes a histogram-based approach towards feature splitting by making a feature value for each feature and finding the optimal spilled point for each branch in the decision tree. Instead of growing level-wise, it expands the leaf with the most reduction in the loss function for higher accuracy. Furthermore, to control the complexity it uses regularization techniques such as max depth, feature fraction, etc. 

Strengths: 

• Efficiency and Speed: This algorithm is especially known for its fast training and its ability to handle large datasets, which is great for when you must work in real-time situations. To do so it uses techniques such as Gradient-based One Side Sampling (GOSS) and Exclusive Feature Bundling (EFB) which enables it to produce accurate results as well. 
• Takes up Less Space: The implementation of histogram-based algorithms and other such efficient data structures allow it to handle large datasets without taking up too much space. 
• Flexible: It offers hyperparameters and customization options so you can mold it according to your situation. 
• Feature Importance Estimation: This built-in feature allows you to understand the relationship between different features and their contribution to the final prediction. 

Weaknesses: 

• Prone to Overfitting: Without sufficient regularization or when you’re working with a small dataset, this algorithm will become prone to overfitting. 
• Sensitive to Hyperparameters: Since it provides many hyperparameters to control the model (and finding the optimal settings can be time-consuming) it becomes sensitive to hyperparameters.  
• Complex: Just like other boosting algorithms the final classifier from this algorithm is also very difficult to interpret. 
• Limited Support for Categorical Features: All categorical features must be converted into numerical values which may become complex to compute when you have a dataset with too many categorical features. 

What are Random Forests And When to Use Them

Just like boosting and all its types, random forest is also another ensemble learning technique. This technique has many applications e.g., anomaly detection, bioinformatics, recommender systems, etc.  

The work behind random forest is that during the training time, it makes a multitude of decision trees by making bootstrap samples from the original dataset that is the same size as the original dataset using a standard decision tree algorithm e.g., Classification and Regression Trees (CART). For every node, a random subset of features is selected. To capture the complexity and patterns in the data, every tree is allowed to grow to the maximum depth without any pruning. For classification tasks, the majority voting technique is used to determine the class with the most votes among the individual trees and for regression tasks, the average of the predicted values from all the trees is taken. 

Strengths: 

• Accurate and Robust: This model provides high predictive accuracy since it uses an averaging and voting mechanism. This also makes this model robust to outliers since the impact of every individual tree is dampened by taking the average. 
• Handles High-dimensional Data: Not only can this algorithm handle datasets with very large numbers of features but it can also handle features that may be irrelevant due to the added layer of randomness.  
• Feature Importance estimation: This provides insights about the feature importance, which allows you to understand the relationship between different features and their contributions to the final prediction. 

Weaknesses: 

• Difficult to Interpret: The evaluation of a multitude of decision trees is not only computationally expensive but the interpretation of the final model also becomes difficult  
• Storage Issues: Since additional memory is required to store the decision trees, working with a large dataset may result in storage issues. 
• Takes Too Much Training Time: In addition to its other weaknesses this algorithm takes a lot of time since it has to individually train multiple decision trees. 

Boosting Vs Random Forests – What to Use?

Now that we have explored both boosting algorithms and random forests, let’s compare them to help you determine the most suitable algorithm for your specific ML scenarios.

Boosting algorithms are ideal when dealing with weak learners, noisy data, or imbalanced datasets. They sequentially train weak learners to create a strong learner that corrects the mistakes of their predecessors. AdaBoost, for example, is versatile and can work with various weak learners like SVMs or neural networks. It is also robust and immune to overfitting. However, it can be sensitive to noise and computationally expensive due to sequential training.

LightGBM is a great choice for time-sensitive tasks or when memory is a concern. Its fast training speed and ability to handle large datasets in real-time situations make it very efficient. It also offers hyperparameter customization options and provides insights into feature importance. However, it may be prone to overfitting without sufficient regularization and can be sensitive to hyperparameter settings.

On the other hand, random forests are suitable for high-dimensional data and perform a wide array of classification and regression tasks. They excel in accuracy, robustness against outliers, and their ability to handle datasets with many features. Random forests also provide feature importance estimation. However, interpreting the model can be challenging due to multiple decision trees, and it requires additional memory for storing the trees. Training time can also be longer since multiple trees are trained individually.

So, it really depends upon the specific task you’re undertaking and you cannot write off either of the two techniques. The best decision can only be made once you fully understand the techniques, their pros & cons, and your requirements.

Wrap Up

Random forests and Boosting techniques such as AdaBoost and XGBoost are powerful ensemble learning techniques that provide top-of-the-shelf results in most cases. However, just like everything else, each comes with its own set of strengths and weaknesses, and it’s important to know that while all are considered very effective and have wide application in the industry, there’s no one-fit-for-all technique that you can use for every scenario.

Throughout the article, we’ve explored various boosting techniques and compared them with random forests to see how they stack up against them. Hopefully, by now, you must have a clear understanding of the strengths and weaknesses of both techniques and where to use them accordingly.