README.md
In DexGroves/hacktoolkit: Generic Hackathon Sauce
hacktoolkit
Potentially useful stuff for the next Allstate Hackathon
Installation
# install.packages("devtools")
devtools::install_github("DexGroves/hacktoolkit")
What's in here
Weighted variance, weighted binomial deviance. weighted_variance, binomial_deviance
A pretty fast implementation of weighted AUC. auc
Logistic conveniences. logit_to_prob, prob_to_logit
Pretty efficient leave-one-out encoding. loo_encode
Somewhat-efficient-but-horrifically-slow-by-nature marginal effect algorithm with sampling. marginal_effect
Tools for training models and scoring out-of-fold for use in downstream models/metamodels. cross_train_predict, train_folds, predict_folds
DexGroves/hacktoolkit documentation built on May 6, 2019, 2:12 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
hacktoolkit
Potentially useful stuff for the next Allstate Hackathon
Installation
# install.packages("devtools")
devtools::install_github("DexGroves/hacktoolkit")
What's in here
Weighted variance, weighted binomial deviance. weighted_variance, binomial_deviance
A pretty fast implementation of weighted AUC. auc
Logistic conveniences. logit_to_prob, prob_to_logit
Pretty efficient leave-one-out encoding. loo_encode
Somewhat-efficient-but-horrifically-slow-by-nature marginal effect algorithm with sampling. marginal_effect
Tools for training models and scoring out-of-fold for use in downstream models/metamodels. cross_train_predict, train_folds, predict_folds
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.