Machine Learning Guide for Oil and Gas Using Python

Chapter 3: Machine learning workflows and types

Abstract

This chapter walks the reader through a step-by-step guide for building a Machine Learning (ML) model. These steps include but are not limited to data gathering and integration, data cleaning (data visualization, outlier detection, and data imputation), feature ranking and selection, data normalization or standardization, cross-validation (including holdout method, k-fold cross-validation, stratified k-fold cross-validation, leave-P-out cross-validation), and blind set validation. Bias–variance trade-off is also discussed with the visualization illustration for building a successful general ML model. Afterward, various ML types such as supervised, unsupervised, and reinforcement learning ...