The genius of machine learning models lies in how simple they are at their core. At the core, ML based models are simply a combination of statistics and computational algorithms. Machine learning models are actually simple when you get down to this core. However, this is bad for you because it means you have many choices when you want to figure out what to do in terms of model selection and model evaluation. How do you choose the right machine learning models for your application?
This is a crucial question because it changes how your application will work. The right selection will be the difference between your model improving things or making your entire application worse. One of the worst things you could do is to go through with the model selection process and come out with a worse result for your application. However, companies do this all the time, which is why model evaluation is such a crucial component of the process.
Kinds of Models
Choosing a model for machine learning is difficult, but it does not have to be that way. You don’t have to go with model based machine learning as well. No matter what you do, there are many options to get to the level of performance you desire.
Model selection will be based on the level of statistical variance you want and the data you have to feed into your algorithm. You choose the correct models based on the test data and the data points you have available. How to select model in machine learning? It is actually a lot simpler than you think, but it requires careful consideration based on your specific use case.
The only way to ever evaluate machine learning models is by seeing how they perform in production. However, before you invest all the time and effort required to take a model into production, model evaluation needs to be streamlined down to a science. You want to ensure you are looking at the factors that make up a good model.
This model evaluation needs to focus on precision, accuracy, and many other factors that lead to your model being useful in a production environment. This is where model based machine learning comes into play, and it allows you to figure out which one will do the job you are trying to accomplish. Good decisions here are the difference between success and failure for model evaluation in machine learning.
Finding the Optimal Model
There are plenty of trade-offs you need to work with in terms of finding the correct machine learning models. The model selection process should be thorough and accurate. When doing model evaluation, you have to ensure that everything is done to maximize the performance of each model. Because of that, you can be confident when you make a decision on a model’s performance.
Data is just one of the many factors, but it is often the most important one when it comes to choosing a good model for your production workloads. You also want to be cognizant of the problem you are trying to solve with ML based models.