Skip to content

In this exercise, the objective is to build a classifier only with the training data, with the goal of achieving the best performance possible on the validation data.

License

Notifications You must be signed in to change notification settings

brainy749/Binary-Classification-Prediction

Repository files navigation

Binary-Classification-Prediction

In this binary classification problem, the objective is to build a classifier only with the training data, with the goal of achieving the best performance possible on the validation data.

We start by considering the following models GLM (Elastic Net with logistic function and regularisation), Gradient - Boosting Machines (gbm), XGboost, Random Forest, multilayer artificial neural network in H2o and then select a lead model with H2o AutoML algorithm.

In this exercise, the best performance model was a gbm (with 81 trees and max depth of 8) giving  AUC of 0.767 on on the validation data.

Target variable is classlabel

Contents:

  • R Markdown file (pmaddo_kreditech_exercise.Rmd)
  • Html Output from Rmd file (pmaddo_kreditech_exercise.html).
  • You can easily read through the html output like a report by selecting Show All code at the top right corner of the html to display R codes and comments :)
  • See Section 3.7.3 for details on the gbm model.

About

In this exercise, the objective is to build a classifier only with the training data, with the goal of achieving the best performance possible on the validation data.

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages