While building a machine learning model can be time-consuming and often takes a considerable percentage of your budget, finishing the project is a significant achievement worth the investment. For companies, machine learning enables them to identify opportunities and minimize risks. For independent developers, machine learning can turn their program more dynamic.
However, no matter how much time and money you spent developing the algorithm and how experienced your data scientists are, there will always be imperfections. Perhaps its accuracy is lacking, or maybe its margin of error may be above average.
Either way, your next step after developing an algorithm is to determine how you can improve its performance. There are several ways to go around this, and that’s what you’ll learn in this quick guide. Below are a few tips on how to improve machine learning performance:
In a machine learning data set, missing values are common, and these inconsistencies can negatively impact machine learning performance, creating confusion on the algorithm. Moreover, the missing values are often random, like when a person forgets to record a particular sale, so there’s no use contemplating how to fix it.
One way to go around this is to replace the missing value with a placeholder. For instance, if the data set consists of gold price in 2020, you can try replacing the missing values with an accurate value like $1,900.
Another inconsistency that may influence the machine learning model’s performance is outliers. Outliers are values that are way far off from the average value.
For example, if the price range of gold is $1,700 to $2,000, values lower than $1,500 would be considered outliers. Fortunately, you can deal with outliers the same way you handle missing values by replacing them with a value more consistent with the data set.
If your goal is to improve machine learning performance, you need a tool to determine whether you’re doing an excellent job at it. One particular metric that can help you with this issue is loss function.
A loss function tells you how far off the model’s predicted values, and the actual values are. If it yields a high value, the machine learning algorithm isn’t performing well.
Consequently, if the loss function shows a low value, it means your machine learning model’s prediction is reasonable. This can serve as a good indicator if you’re getting closer to your goal.
In machine learning, a hyperparameter refers to the variables that control the overall behaviour of the machine learning model. Hyperparameters are set before the algorithm runs, so your ultimate goal is to make sure you find the ideal combination and value of hyperparameters before starting the operation.
Doing so minimizes the risk of errors within the model. Since hyperparameters tend to become more complex as the algorithm continues to learn, you need to adjust them as early as possible.
Another way you can improve the machine learning algorithm’s performance is to apply feature selection. For your reference, the term ‘features’ is used in machine learning to refer to the characteristics of the object being observed. Feature selection is a part of the entire feature engineering process that deals with the prioritization of features.
It would be best to use this practice to improve machine learning performance because not all features are essential, and you can’t possibly spend time on each one of them. Also, too many features will result in overfitting. Most of them might be necessary, but a few are outright pointless.
Your goal is to determine which is which and filter out the least important features. When selecting a part, you need to consider its relevance to your algorithm—though you can also use an application that automatically shows you the importance of features.
Focusing on one machine learning algorithm is the ideal approach, but that’s easier said than done. If you’ve tried everything but still can’t improve the algorithm’s performance, you might want to consider implementing ensemble learning.
Ensemble learning is the practice of creating other algorithms and testing them in conjunction with each other. Doing so allows you to achieve higher performance compared to when you use a unique algorithm.
Machine learning is an incredibly complex process. It requires an assortment of skills, and more often than not, even data experts find it challenging to improve their machine learning models.
Plus, developers are often tempted to start a new project when their current model isn’t doing well, which is understandable. However, with these tips, you should be able to get out from the rut you’re in and start making some actual progress with your algorithm.
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