Developing machine learning models even for the most qualified and experienced data scientists is utterly complicated. Because of this, there's always the likelihood that you'd make a mistake during the entire process. And, when this happens, the accuracy and reliability of the machine learning models are affected. As a result, such machine learning models won't be able to provide you with quantifiable and real-life value.
However, there are proven ways you can follow to optimize the performance of machine learning models to get correct and reliable results. Apart from using a reliable machine learning operating system, such as the one offered by cnvrg.io, here are effective ways you can boost the performance and accuracy of your machine learning model:
1. Select A Robust Algorithm
Most real-world datasets are complicated. Because of this, you need a robust algorithm that can comfortably learn and analyze the data to make correct predictions. Doing this is critical because machine learning algorithms perform similarly to machine learning engines. And, with the robust algorithm, you can be confident that your model will deliver a decent performance.
However, knowing the best algorithm to choose is often one challenge that'll come your way. This is because different algorithms are designed to work best with different kinds of datasets. To help you pinpoint the best algorithm to choose, you need to ask yourself a couple of questions, including:
- What problem is it you want to solve?
- What computing resources are available?
- What business goals do you want your machine learning model to accomplish?
- How scalable do you want your machine learning model to be?
- What's the size and type of data you want to process?
Once you answer these questions, you'll be better positioned to know which algorithm is the best to choose for your machine learning model.
2. Use Big And High-Quality Training Datasets
The data you feed your data model determine the overall accuracy of its results. This is why you must always use high-quality data sets; otherwise, you won’t get accurate results. And, to test the quality of the training datasets, there are two approaches you can follow, and these are:
- In-depth quality assessment
- Standard quality assurance
After using these methods to measure the quality of the training datasets, you'll be able to get an accurate and unbiased decision from your machine learning model.
3. Add More Data
Another effective approach to optimizing the performance of your machine learning model is by adding more data samples to the algorithm. This way, you'll be able to pinpoint any variance and correlation in your data. This is achieved because more training data allows the machine learning algorithms to understand the different circumstances better. As a result, it'll be able to give you an accurate response after an exhaustive comparison.
When adding more training data, you should look to add those across a wide scenario to avoid making any prejudiced decision. However, you shouldn't add data to your machine learning algorithm. Instead, you must first determine the quantity and type you want, and while doing this, make sure to consider different factors, such as:
- Model skill
- The problem's complexities
- Data size evaluation
- Learning algorithms
- Use of statistical heuristic rule
With enough training data, you'll increase the chances of your machine learning model performing well. Nonetheless, you must remove missing values and outliers that might negatively affect your model's accuracy. Doing this will help you make unbiased and correct decisions.
4. Apply Feature Engineering
One of the best ways to optimize the performance of your machine learning model is through feature engineering, which means generating new features from your current ones. This can be done by combining several existing features into one or a couple of features. For instance, you can use existing features, such as workplace location and home address, to create a feature that shows how far a person lives from the office.
Feature engineering is useful because it helps explain the machine learning model's variance. It boosts the model's accuracy by making it better to make correct predictions. There are different approaches you can follow during feature engineering, and your datasets determine what's the best option to choose. And, to help you decide, here are some helpful tips to remember:
- Use data pre-processing techniques, such as data selection and extraction, to pick out your dataset's most useful features.
- Attempt to check for correlations between different features to develop new ones that better show these connections.
- Use transformations, such as standardization or logarithmic transformation, for your features to improve the compatibility and ease of working with the features.
5. Validate And Revalidate The Machine Learning Model
Developing a machine learning model isn't all you need to make correct forecasts. Instead, you have to make sure that the model is accurate by validating it to get the right results. Only by doing this will you be in a position to boost the machine learning model's performance.
With this in mind, you should make it a habit to validate and revalidate your machine learning model routinely. Doing this will allow you to identify the correct parameters to measure precision and accuracy. You'll also determine whether the algorithm you're using is the right one for your needs.
But, before you start validating and revalidating your machine learning model, first identify the ideal validation method. You must do this to ensure that the validation method you pick can authenticate your machine learning model. Consequently, this helps boost the overall performance of your machine learning model and ensures the subsequent results are impartial.
Validating the machine learning model can either be done automatically or manually, depending on the model you're using or the validation team. You must take this into consideration to ensure this task isn't biased.
While improving the accuracy of your machine learning model is crucial, optimizing its performance is often not that simple and straightforward. Nonetheless, you shouldn't lose hope just yet, thanks to this detailed guide that’s highlighted the different ways you can optimize the performance of your machine learning model. Once you do this, you'll be able to get better and more accurate results that are practically useful.