What is machine optimization?

Optimization is the problem of finding a set of inputs to an objective function that results in a maximum or minimum function evaluation. It is the challenging problem that underlies many machine learning algorithms, from fitting logistic regression models to training artificial neural networks.

What is optimized model?

An optimization model is a translation of the key characteristics of the business problem you are trying to solve. The model consists of three elements: the objective function, decision variables and business constraints.

When should I update my model?

After a month, quarter, or a year it is necessary to update predictive models with new data. As these new patterns emerge its important to periodically take time to investigate your data, update your models, and challenge your assumptions about your business.

How to improve the accuracy of a machine learning model?

Now we’ll check out the proven way to improve the accuracy of a model: 1. Add more data Having more data is always a good idea. It allows the “data to tell for itself,” instead of relying on assumptions and weak correlations. Presence of more data results in better and accurate models.

How do I keep my machine learning models up-to-date?

Another way to keep your models up-to-date is to have an automated system to continuously evaluate and retrain your models. This type of system is often referred to as continuous learning, and may look something like this: Save new training data as you receive it.

How to improve the accuracy of a model?

Let’s dig deeper now. Now we’ll check out the proven way to improve the accuracy of a model: 1. Add more data Having more data is always a good idea. It allows the “data to tell for itself,” instead of relying on assumptions and weak correlations. Presence of more data results in better and accurate models.

What are the challenges of continual learning in machine learning?

One major challenge with continual learning is how to deploy new models to the same environment without negatively affecting users experience and maintaining high accuracy. Deploying models for continual learning is a bit different from classic model deployment.