5 Ways To Improve Machine Learning Model Performance

Businesses today depend on machine learning to optimize and scale their operations. When using this analytical tool, you’ll be able to generate essential data-driven insights. Thanks to this, you’ll be better placed to make informed decisions that can propel your business to rapid growth. 

With that said, creating a high-performance machine learning (ML) model is quite challenging. It’s even more challenging to boost the performance of a machine learning model to produce reliable and correct results. Data scientists acknowledge this as they often face a hard time testing a model’s performance to increase its accuracy. If your ML model is struggling to deliver accurate and reliable results, here are five effective ways to boost its performance. 

1. Add More Data 

One way you can improve the performance and accuracy of the cnvrg.io platform in your machine learning model, you can add more data samples. With more data, you’ll be able to create greater correlations. This allows you to get a better and clearer perspective from the overall data, thus making it more accurate and reliable. 

For you to determine the type and quantity of training data sets you need, there are a couple of factors you’ll need to consider, including; 

  • Data size evaluation 
  • Use of statistical heuristic rule  
  • Model skill 
  • Learning algorithms
  • Complexities of the problem 

Typically, any machine learning model which uses insufficient training data or low-quality data performs poorly. Because of this, you can also take your time to remove outliers and treat missing values as this would affect the accuracy of your model. Once you do this, you can be certain that your training data will give you accurate results and avoid unbiased decisions.

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2. Select A Robust Algorithm 

Machine learning algorithms act like machine learning engines. The data fed into the algorithm assists the model to learn and make accurate predictions. Knowing this, you shouldn’t overlook the importance of choosing a robust algorithm in improving a model’s predominance. 

The challenge you’ll have to deal with is deciding which algorithm to choose. When in such a situation, here are some questions you should ask yourself to help you pick a suitable option; 

  • What are the available computing resources? 
  • What’s the problem you’re trying to solve? 
  • How scalable should your model be? 
  • What’s the type and size of data you’re processing? 
  • What are the business objectives you’re hoping your model achieves? 

By answering these questions, it’ll be easier to figure out which algorithm is best suited for your model.

3. Validate And Revalidate Your Machine Learning Model 

You can also improve the accuracy of your machine learning model by constantly validating and revalidating it regularly. After all, you can only test your model’s accuracy by constantly testing it and confirming that it’s working correctly. Testing your model at regular intervals helps you define the correct parameters to gauge its efficiency, precision, and accuracy. It also helps you know whether you picked the correct algorithm.    

Because there are different validation techniques, it’s essential to ensure that the selected option is ideal for authenticating your ML model. You must do this to ensure your predictions are unbiased and enhance your ML model’s overall performance. You can validate the ML model either manually or automatically, which depends on the validation team and the kind of model available that does this task to make sure it’s unbiased. 

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4. Change Feature Selection 

Another way of improving the performance of your ML model is through feature selection. This process helps identify the best subset of qualities that better clarify the relationship between the target and independent variables. Features significantly affect your model’s output and are an essential part of building a model. You can boost your model’s performance by looking for a suitable feature or variable which can be obtained as new knowledge.   

5. Ensemble Models Method 

Choosing a robust machine learning algorithm is one way to guarantee accurate results. However, algorithms differ, with some having a better ability to handle certain data sets, unlike others. Because of this, combing different algorithm models make sense to get results of greater accuracy. 

The ensemble technique entails the use of different models to better the precision using bagging and boosting. This means the subsequent results are of greater quality as opposed to if you used a specific model. The ensemble models method usually improves your model’s accuracy as it’s a lot more complicated than other conventional methods. In addition, this technique provides you with suitable base levels through which you can draw and improve your ML model’s ensembles. 


It’s a fact that improving an ML model’s performance isn’t easy. But if you want to become a master data scientist, you’ll need to understand how this gets done. This in-depth post has taken you through insightful ways to improve the performance of your machine learning model. Consequently, your model will constantly deliver accurate and reliable results that’ll help steer your business in the right direction.

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