In a recent survey, 44% of executives said that the most important benefit of artificial intelligence (AI) is that it provides information to make better decisions. AI (and machine learning, its most widely spread subset) is the perfect ally to sift through large datasets in search of insightful patterns. In fact, most of its current usage has to do with that goal.
With machine learning algorithms taking such a crucial role, the accuracy and sophistication of AI models becomes more important than ever. Can you imagine creating a model to help you out to make important business decisions only to find out that it’s biased and inaccurate? That’s precisely the problem that Python and React developers have to deal with when training machine learning platforms: the problem of underfit data models.
The foundations of a strong machine learning model
Before getting into the details of underfitting and what it means for machine learning algorithms, it’s important to understand a couple of crucial concepts. First and foremost, you need to know what a model actually is. Though you can find several definitions online, the simplest would say that a model is a system that maps inputs to outputs.
For instance, you could develop a model that calculates the potential revenue of a marketing campaign given the time of year you put it out. The underlying hypothesis of that model would be that launching the campaign in different moments throughout the year would lead to different results. Thus, you could see a model as a way to learn the connections between the inputs and outputs and how they relate to each other.
With an accurate model, you could predict accurate outputs (the ROI of the marketing campaign) for new inputs (the different times of year you could launch the campaign). The inputs are called “features” and the outputs are called “labels” and the model maps the connection between both of them.
Now, for a model to be successful, it has to learn how to detect the patterns and relationships that keep features and labels tied together. That’s done through a training phase, in which the model is fed with a training set of features so it can start “learning” to detect the connections. The training set already has some known connection between features and labels.
If you only fed that training set to the machine learning model, you’d be saying to the algorithm that the relationship is linear (as in, the features always produce the outputs in the way presented in the data set). However, that’s hardly the case in real life – if at all. In reality, any process has data that doesn’t fit the trend, that’s alien to the connection between features and labels. That data is called noise, variables that aren’t taken into account by the model because it doesn’t matter or because it’s purposefully ignored.
In the marketing campaign example, the trend between ROI and the time of launch is linear. Yet, in reality, that could hardly be the case, as there are other factors that influence the relationship (ad platforms used, industry niches, amount of money spent, and so on).
A model that’s trained properly will detect the connection in such a way that it ignores the noise. If it does it, then the machine learning model will be able to draw generalizations from new features (that is, to provide sensible labels from data that didn’t come from the training set). When it can’t make those generalizations, you might see problems, one of which is what Python and React development services call underfitting.
The importance of properly trained models
Underfitting happens whenever a machine learning model hasn’t learned enough about the relationships and connections between features and labels. This would lead to the model not being able to predict precise outputs, with the most deviations happening in those features that are the most different from those in the training set.
An underfit model has low variance and high bias. Variance is how much the model depends on the data from the training set. In an underfit model, the variance is low because the model didn’t have enough time to truly understand the connections between features and labels. Thus, it doesn’t depend on the training data, simply because it acts as if it was ignoring it.
On the other hand, you can say that an underfit model has a high bias because it makes strong assumptions about the data. In the marketing example, that could mean that launching the campaign in May is the best move, but since the model is underfit, it didn’t take the time to learn that December might be a better choice. It shows bias because it fails to recognize the subtle nuances of the training set.
You surely can see where this is going. An underfit model is a huge problem because it offers outputs that aren’t even close to reality. If you took important business decisions based on the insights provided by an underfit model, you’d be taken the wrong paths because you’d be misinformed. You’d be better off by making those decisions using your gut!
So, it’s highly important that your Python and React development teams look into the machine learning models they are developing for you and see if they are underfit. If they are, then you need to use one of the potential solutions to correct the issue. You can keep training the model but keeping an eye on the process – in machine learning, there’s such a thing as too much learning, which might lead to overfitting.
You could also add more features to increase the accuracy of the end model. You could also revise the data preprocessing stage to see if the problems in the analysis don’t come missing or if there is corrupt data that could be cleaned beforehand. Finally, you could add more analysis instances to get to a deeper understanding of the connections between features and labels.
The bottom line is that underfit data models are a threat to businesses using machine learning to make decisions. That’s because underfitting can’t predict outcomes with accuracy, which leaves you with highly biased assumptions that can truly become a problem should you act on the resulting insights.
Understanding the underfitting problem and taking the necessary steps to correct it is the only way you can truly get improved decisions.
