Building a successful machine learning model is not just about using fancy algorithms. One of the most important factors is also one often overlooked: the input. It sounds obvious, but a model is only as good as the data it’s built on.
In this post, we’ll delve into why gathering high-quality input is so crucial to success and how to approach it in a way that ensures the best possible results.
Before diving into any modeling, it’s essential to have a clear understanding of the problem you’re trying to solve and the success metric you’ll use to evaluate the model. The problem statement should be specific and outline the issue you want to address or the behavior you want to encourage. The success metric should align with this problem statement and measure the model’s effectiveness.
For example, let’s say you’re building a churn model for an e-commerce website. Your problem statement could be “reduce customer churn by predicting which customers are at risk of not making a purchase within the next three months.” Your success metric could be the percentage of at-risk customers that the model accurately identifies.
Without a clear problem and success metric, it’s easy to get sidetracked and build a model that doesn’t address the actual issue or measure its effectiveness correctly.
To build a successful model, you need to have a deep understanding of your customer and their behaviors. This includes the customer lifecycle, like how they become acquainted with your product, why they keep using it, and why they stop using it. You should also consider what happens right before they stop using it or when they start using it. This understanding will inform the features you include in the model and how you approach the problem.
This is called descriptive analysis, a critical input step which involves the process of organizing, summarizing, and interpreting data to get a better understanding of your customer. This can include things like looking at demographics, customer behavior patterns, and other characteristics.
For example, let’s say you’re building a retention model for a news website. You might find that certain types of articles lead to higher engagement and longer user sessions. This information could inform the features you include in the model and how you approach predicting retention. If you know that articles about celebrities tend to have lower engagement, you might consider excluding them as a feature in your model.
Or, here’s another example: let’s say you’re building a model to predict which customers are most likely to make a purchase on your e-commerce website. You might find through descriptive analysis that customers who visit your site on weekends are more likely to make a purchase than those who visit during the week. This information could inform the features you include in your model, such as whether or not a customer visited the site on the weekend.
Without understanding your customer and their behaviors, it’s tough to build a model that accurately predicts outcomes or suggests effective actions.
It’s tempting to want to throw every feature you can think of into a model and see what sticks. However, this approach often leads to overly complex models that are hard to interpret and may not perform well. Instead, we recommend building your model incrementally and evaluating its performance at each step.
Start with a basic model and add features one at a time, paying attention to how each impacts the model’s performance. If a feature doesn’t improve the model’s effectiveness, consider giving it the boot. It’s also essential to keep an eye out for features that are negatively correlated or seem like noise, as they can hinder the model’s performance.
By building and evaluating your model incrementally, you can avoid the pitfalls of overly complex models and focus on the features that truly impact the model’s performance. It’s like building a house – you want to make sure each foundation is solid before adding on more floors.
External factors, like weather or economic trends, can impact a model’s performance. While it’s not always possible to control these factors, it’s crucial to consider their potential impact on the model and how to incorporate them appropriately.
For instance, let’s say you’re building an engagement model for a ride-hailing company. The weather could be an important factor to consider. If it’s raining, there may be higher demand for rides. In this case, incorporating weather data as an input to the model could be beneficial.
On the other hand, if the weather isn’t a significant factor in your business, it might not be worth the added complexity to include it in the model. It’s important to strike a balance between including relevant external factors and keeping the model as simple as possible.
Another example could be if you’re building a model to predict sales for a retail store. In this case, economic trends could be a relevant external factor to consider. If the economy is performing poorly, consumers may be less likely to make big purchases, which could impact the model’s predictions.
It’s also essential to consider the time frame of your model and whether external factors may change significantly within that time frame. If you’re building a model to predict sales for the next year, it might not make sense to incorporate economic trends from the past decade.
Incorporating external factors correctly can improve the accuracy of your model, but it’s important to be mindful of how they fit into the overall problem and success metric.
By focusing on the input, you can build a model that accurately addresses the problem at hand and effectively measures its success.
At Measurly, we believe that the input is a crucial part of building a successful machine learning model, which is why we prioritize it in our process. If you’re interested in learning more, we’d love to hear from you.