Creating the Whole Machine Learning Pipeline with PyCaret

 By Medium - 2020-12-03

Description

This tutorial covers the entire ML process, from data ingestion, pre-processing, model training, hyper-parameter fitting, predicting and storing the model for later use. We will complete all these…

Summary

  • Recreating the entire experiment without PyCaret requires more than 100 lines of code in most libraries.
  • It is directly from the PyCaret datasets, and it is the first method of our Pipeline Image by Author from pycaret.datasets import get_datadataset = get_data('credit')#check the shape of datadataset.shape In order to demonstrate the predict_model() function on unseen data, a sample of 1200 records from the original dataset has been retained for use in the predictions.
  • There are 6,841 samples in the test set.
  • 4- Create the Model Image by Author create_model is the most granular function in PyCaret and is often the basis for most of PyCaret's functionality.

 

Topics

  1. Backend (0.25)
  2. Database (0.13)
  3. NLP (0.11)

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