Building machine learning models that are capable of generalizing on any future data requires a lot of prior consideration and accurate assumptions about the feature set and available training algorithms. There are various models that can automate the process of analytical model building.
Let’s have a look at the key steps involved in building a model:
Even though it is true that a complex machine learning model can learn a variety of features, it also makes the model very hard to understand. Therefore, it is a good practice to keep the first iteration of the model relatively simple to try to get the base right. Start with a simple model that lays down a strong foundation for future, more complex models. Choose a simple feature set that will make it easier for you to understand and evaluate your model over whether or not it is learning the required features correctly and effectively.
It is a common approach to start with a pre-existing machine learning model and train that model further on your own custom data set. However, it is necessary to conduct several tests on the exported model to see if there are any defects as it needs to be ensured that the model performs well on “hold-out” data.
Choose the model metrics that are easy and make the model easy to understand and evaluate. Start by choosing metrics to train your model on simple objectives and then add further layers to incorporate additional logic.
The first model that you build will not be the last model that you will launch. Evaluate if your model requires an update or additional complexity and, if necessary, relaunch the model. A model can be relaunched if:
Any new feature is incorporated in it.
Its existing parameters are tuned and the model is re-evaluated as a whole.
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