Machine Learning Guide for Oil and Gas Using Python

Random forest

Random forest is another powerful supervised ML algorithm which can be used for both regression and classification problems. The general technique of random decision forests was first proposed by Ho in 1995 (Kam Ho, 1995). Random forest is an ensemble of decision trees or it can be thought of as a forest of decision trees. Since random forest combines many decision tree models into one, it is known as an ensemble algorithm. For example, instead of building a decision tree to predict EUR/1000 ft, using a single tree could result in an erroneous value due to variance in predictions. One way to avoid this variance when predicting the EUR/1000 ft is to take predictions from hundreds or thousands of decision trees and using the average ...