Random Forest using R – Prediction of Employee Attrition


Random Forest using R – Prediction of Employee Attrition, Learn Random Forest using R and Predict Employee Attrition using a case study.

Course Description

Random forest in Python offers an accurate method of predicting results using subsets of data, split from global data set, using multi-various conditions, flowing through numerous decision trees using the available data on hand and provides a perfect unsupervised data model platform for both Classification or Regression cases as applicable; It handles high dimensional data without the need any pre-processing or transformation of the initial data and allows parallel processing for quicker results. The unique feature of Random forest is supervised learning. What it means is that data is segregated into multiple units based on conditions and formed as multiple decision trees. These decision trees have minimal randomness (low Entropy), neatly classified and labeled for structured data searches and validations. Little training is needed to make the data models active in various decision trees.

The success of Random forest depends on the size of the data set. More the merrier. The big volume of data leads to accurate prediction of search results and validations. The big volume of data will have to be logically split into subsets of data using conditions exhaustively covering all attributes of data.

Decision trees will have to be built using these sub-sets of data and conditions enlisted. These trees should have enough depth to have the nodes with minimal or nil randomness and their Entropy should reach zero. Nodes should bear labels clearly and it should be an easy task to run through nodes and validate any data.

We need to build as many decision trees as possible with clearly defined conditions, and true or false path flow. The end nodes in any decision tree should lead to a unique value. Each and every decision tree is trained and the results are obtained. Random forest is known for its ability to return accurate results even in case of missing data due to its robust data model and sub-set approach.

Any search or validation should cover all the decision trees and the results are summed up. If any data is missing the true path of that condition is assumed and the search flow continues till all the nodes are consumed. The majority value of the results is assumed in the case of the classification method and the average value is taken as a result in the case of the regression method.

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