Evaluating the model

We will now move on to collect evaluation metrics for our model, to add to the metadata of the model.

We will work on the evaluate_model.py file. You can follow along by working in an empty file or by going to https://github.com/PacktPublishing/Machine-Learning-Engineering-with-MLflow/blob/master/Chapter08/psystock-training/evaluate_model.py. Proceed as follows:

1. Import the relevant packages—pandas and mlflow—for reading and running the steps, respectively. We will rely on importing a selection of model-evaluation metrics available in sklearn for classification algorithms, as follows:

   import pandas as pd
   import mlflow
   from sklearn.model_selection import train_test_split
   from sklearn.metrics import  \
       classification_report, \
       confusion_matrix, \
       accuracy_score, \
       auc, \
       average_precision_score, \
      ...