A code example walkthrough of preparing the data, training, evaluating, and saving an ML model using the Evidently Python library.
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Video 2. Train and evaluate an ML model [OPTIONAL CODE PRACTICE], by Emeli Dral
In this video, we prepare the data, train, evaluate, and save an ML model that we will use later in this module to deploy an end-to-end pipeline for data and ML model quality checks.
Want to go straight to code? Here is the example notebook to follow along.
Outline: 00:00 Introduction 00:39 Imports 01:44 Load and preview the raw data 05:02 Feature engineering function 17:33 Split into train, reference and production 19:48 Transform raw data into pre-processed (and some debugging!) 23:00 Model training 27:28 Evaluate model quality
