Patterns searching and binary classification in economic and financial data is a large field of research. There are a large part of the data that the target variable is binary. Nowadays, many methodologies are used, this package collects most popular and compare different configuration options for Linear Models (LM), Generalized Linear Models (GLM), Linear Mixed Models (LMM), Discriminant Analysis (DA), Classification And Regression Trees (CART), Neural Networks (NN) and Support Vector Machines (SVM).
OptimClassifier provides a set of tools for creating models, selecting the best parameters combination for a model, and select the best threshold for your binary classification. The package contains tools for:
as well as others that will be implemented in the future.
For this, you can choose different packages such as:
This is a basic example which shows you how to solve a common credit scoring problem with this package:
## Load a Datasetdata(AustralianCredit)## Create the modelcreditscoring <- Optim.GLM(Y~., AustralianCredit, p = 0.7, seed=2018)#See a ranking of the models testedprint(creditscoring)#Access to summary of the best modelsummary(creditscoring)#Do not sure of like the best model??, you can access to the all model, for example the 2nd modelsummary(creditscoring,2)
If you find problems with the package, or there's anything that it doesn't do which you think it should, please submit them to https://github.com/economistgame/OptimClassifier/issues. In particular, let me know about optimizers and formats which you'd like supported, or if you have a workflow which might make sense for inclusion as a default convenience function.
Optim.LMMfunction, added more options
includedataoption in training functions
Optim.NNfunction for apply in the future training. This change occurs to fully align with CRAN policies.