The business generalist or expert in a specific aspect of business operations might wonder about machine learning and data science. Random Forest and XGBoost are two techniques that are related and commonly used in business predictive modeling. I want to give you some basics for these frequently used techniques so you can be sharp enough to know what’s under the hood.
The first thing you need to know is that Random Forest and XGBoost predictive models are classification algorithms. The models classify inputs into predicted outputs and provide the probability that a set of inputs will be classified in a targeted way.
(I won’t get into backtesting and proving the predictive power of the model in this post, nor will I delve into how tree-based models work. Both of these topics are more technical and are in the domain of the data scientist you will be working with.)
To achieve this, you take an outcome that you have seen in the past, like a pool of clients flagged as converters and non-converters. Then, you attach attributes to each client (e.g., geography, income, purchase behavior, gender, age, etc.). The model assesses each client and classifies them into converter or non-converter buckets based on the attributes.
(I am focusing on a conversion model here, but it could be anything that you want to classify: what color a customer prefers, how likely a client will cancel a contract, what portion of a supplier’s delivery will fail quality control, how likely a patient with gum disease is to have diabetes, etc.)
The strategic opportunity with these models is to classify prospective subjects into positive and negative outcomes so that in the future you can predict the likelihood that the positive event (conversion in this example) will occur when you ask the model to evaluate a new set of prospective clients, which the model has never seen before.
In other words, the model learned to classify a prospective client as a converter or non-converter, and when presented with a brand-new prospective client associated with a set of attributes (again, geography, income, purchase behavior, gender, age, etc.), the model tells you the likelihood of a positive outcome and therefore gives you direction on where to focus your sales and marketing investment (in this example).
When choosing between Random Forest and XGBoost, there are factors to consider. Personally, as I have mentioned in other articles, a good data science team will not go down just one algorithmic path. Instead, the team will try different models and go through an iterative process of choosing a model that works best. Incidentally, the type of model might also change in the future based on the dynamics of the real world, so re-testing algorithms as part of the predictive modeling program is recommended. Besides Random Forest and XGBoost, other classification techniques include logistic regression, neural networks, and k-nearest neighbors, to name a few.
Back to Random Forest and XGBoost. Here is an overview of the differences to hone your knowledge.
Nature of the Data: Different types of data might be better suited for different algorithms. For example, if your data has a lot of categorical variables (gender, drink preference, color preference, etc.) or nonlinear relationships, XGBoost might be more appropriate due to its ability to capture complex patterns through gradient boosting. On the other hand, Random Forest might perform well with simpler, less structured data.
Computational Resources: XGBoost can be computationally intensive, especially for large datasets or when using many boosting rounds. If you have limited computational resources, Random Forest might be a more practical choice as it tends to be faster to train and requires fewer hyperparameters to tune.
Interpretability Requirements: Random Forest typically provides more straightforward interpretability compared to XGBoost. Each tree in a Random Forest can be examined to understand feature importance and decision paths. If interpretability is crucial for your application, Random Forest might be preferred.
Specific Use Case Considerations: Depending on the specific use case or domain, one algorithm might be more suitable than the other. For instance, if you’re working on a time-sensitive project and need to quickly build a model with decent performance, Random Forest’s simplicity and ease of implementation might make it the preferred choice. Conversely, if your goal is to squeeze out the last bit of predictive performance from your model and you have the computational resources to support it, XGBoost might be worth the additional effort.
In summary, if you are faced with an investment decision and have data that can be used to predict positive and negative, or preferential classifications, you can use data science to build a predictive classification model. Use this technique to focus your investment into the subjects that present the highest probability of positive classification.
And don’t forget that the choice of algorithm also depends on practical matters you are facing.
While XGBoost is often seen as an advancement over Random Forest in terms of predictive performance and flexibility, the decision between the two algorithms should be guided by factors such as the nature of the data, available computational resources, interpretability requirements, and specific use case considerations. In some scenarios, the simplicity and ease of implementation of Random Forest might outweigh the potential performance gains of XGBoost.
***FAQs***
What sets Random Forest and XGBoost apart from other classification techniques discussed? Random Forest and XGBoost stand out among the mentioned classification methods due to their unique abilities to handle intricate patterns and nonlinear relationships within data. While logistic regression and neural networks are valuable, Random Forest and XGBoost offer enhanced capabilities in capturing complex data interactions, making them particularly advantageous in business predictive modeling scenarios.
How does one decide between Random Forest and XGBoost for a predictive modeling project? Choosing between Random Forest and XGBoost hinges on several factors, including data characteristics, computational resources, interpretability needs, and specific project requirements. While XGBoost may offer superior flexibility and predictive power, Random Forest could be preferred for its simplicity, faster training times, and easier interpretability, especially when dealing with less structured data or limited computational capacity.
What strategic advantages do Random Forest and XGBoost bring to businesses in predictive modeling? Random Forest and XGBoost enable businesses to strategically classify events, such as predicting future outcomes based on historical data. By leveraging client attributes or past behaviors, these techniques help businesses allocate resources effectively by identifying prospects with the highest conversion potential. The iterative process of model selection and testing ensures adaptability to changing market dynamics, maximizing the strategic utility of Random Forest and XGBoost in business applications.
See my other post on XGBoost and Survival Regression here.
About Me
In my role as a CFO, I’ve steered through intricate financial problems, spearheading growth initiatives and optimizing shareholder value for various companies. Leveraging my proficiency in analytics and data science, I specialize in delivering actionable insights that inform strategic decision-making processes. Let’s connect on LinkedIn to explore how my expertise as a Fractional CFO can bolster your company’s growth trajectory with CFO PRO+Analytics.
