Machine Learning
7 Common Machine Learning Mistakes and How to Avoid Them
Machine learning is a powerful tool for businesses — one that can change everything.
It's not just about big data and predictions, machine learning has the potential to automate tasks and help you make better decisions. But there are some common machine learning mistakes companies make when implementing it for the first time.
Here are seven of them and how to avoid them.
One of the biggest mistakes in machine learning is thinking that it's magic. Although you might find some marketing materials claiming that you can use their product to solve any problem, this simply isn't true. You need talent in order to make it work for your business because it requires mathematics and computer science.
For example, a marketing department might want to know which emails from their customers are likely to convert. If you have a machine learning engineer experienced with data labeling, it may be possible for them to train an algorithm based on the email content and label it as "strategic" or "uninterested."
But perhaps your engineering team isn't familiar with these techniques. In this case, you might have to hire a data scientist or machine learning engineer from outside of your company.
Machine learning is only valuable if there's a model to train and use for later predictions — so be sure that one exists before moving forward with implementation.
If the model doesn't already exist, you'll need someone with technical knowledge to build it. If the model does already exists but needs updating or improving, you'll also need a machine learning expert for this task.
So before you embark on a journey with machine learning, be sure that one of these two things is in place: 1) a model to train the algorithm with data labeling and 2) a technical team like Bitstrapped that can build the model from scratch.
One of the most common mistakes companies make is not using all their data.
At first glance, this seems like it contradicts the section about having a model. But there's a difference between not using all your data and not using all of your data for training purposes.
If you're selling to consumers, this may mean that you need to capture more email addresses or purchase behaviors in order to use machine learning techniques on them later. However, if your company is a B2B service provider, then you may need to team up with another company that's related to your target customer in order to learn more about their behavior.
Once you have more data for training, look into how you can incorporate it into your model. If there are parts of the data that don't relate to what you're trying to predict, this might skew your results.
One common mistake is incorporating only negative feedback into the model to train it.
Keep in mind that you'll need positive feedback for your model as well so that it can understand what "correct" looks like. To do this, include both types of feedback when building your model or add a step after you've trained the model to normalize the data so that it's balanced.
At this point, you can continue on with actioning on negative feedback and reporting on positive feedback. However, if the goal is to continually update and improve your machine learning algorithm, then incorporating both types of feedback will be useful.
Another common mistake is using only part of your data when creating the model, which can lead to inaccurate results or bias in your model.
For example, if you're trying to predict whether customers are likely to purchase something, then the data that's used must contain both customers who have purchased the product and those who have not.
If you use only part of your data, there's a chance that the model will contain bias because it wasn't trained on all of the available information. In addition, using too little data may lead to inaccuracies when making predictions — which could also mean that your model contains bias.
The last common mistake we'll cover is taking model predictions literally instead of understanding that they're probabilities.
For example, if you've got a model that predicts whether or not it'll rain tomorrow and the model says there's an 80 per cent chance of rain, then it's not accurate to say that it's going to rain 80 per cent of the time.
A machine learning algorithm is more likely to be accurate when operating on probabilities instead of specific numbers — so don't try to force your model to predict exact values.
Even if you have a high-quality model, there are some cases where you cannot make accurate predictions. In these cases, you should focus on understanding trends instead of precise numbers.
Machine learning is a complex field and it takes time to get results. It's not something that you can apply instantly, or overnight. You have to have enough patience to let the solution grow from data, instead of trying to force the data into a specific solution.
For example, if you have a fraud ring in your system, it may be tempting to try to train your machine learning model to detect fraud immediately.
While this is possible, it's also risky because fraud can take many forms. Since fraudsters are always changing their methods, training the model on only one type of fraud won't necessarily help you discover the next type of fraud.
Instead, focus on building a model that can detect the general characteristics of fraud and how those characteristics change over time. Since you can use data as a feedback loop to update and improve models, you'll likely always have the latest techniques at your disposal for detecting fraudulent activity.
So now that we've covered seven common machine learning mistakes, hopefully, you're able to avoid them in your own business.
Keep in mind that machine learning is a delicate process, so if you don't have the right data or the right model it won't work.
When you're ready to learn more, contact us to dive deeper into machine learning. A 30-minute discovery call with our experts is free.
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