Choosing the right ML model to train data
Similar to data experimentation and preprocessing, training ML model is an analytical, step-by-step process. Each step involves a thought process that evaluates the pros and cons of each algorithm according to the results of the experimentation phase. Like in every other scientific process, it is recommended that you come up with a hypothesis first and verify whether this hypothesis is true afterward.
Let's look at the steps that define the process of training an ML model:
- Define your ML task: First, we need to define the ML task we are facing, which most of the time is defined by the business decision behind your use case. Depending on the amount of labeled data, you can choose between non-supervised, semi-supervised, and supervised learning, as well as many other subcategories.
- Pick a suitable model to perform this task: Pick a suitable model for the chosen ML task. This includes logistic regression, a gradient-boosted...
