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‎tutorials/data/allyears2k.csv‎

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‎tutorials/deeplearning/README.md‎

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* Deep Learning Tips & Tricks
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## Introduction
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This tutorial shows how a H2O [Deep Learning](http://en.wikipedia.org/wiki/Deep_learning) model can be used to do supervised classification and regression. This tutorial covers usage of H2O from R. A python version of this tutorial will be available as well in a separate document. This file is available in plain R, R markdown and regular markdown formats, and the plots are available as PDF files. More examples and explanations can be found in our [H2O Deep Learning booklet](http://h2o.ai/resources/) and on our [H2O Github Repository](http://github.com/h2oai/h2o-3/).
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This tutorial shows how a H2O [Deep Learning](http://en.wikipedia.org/wiki/Deep_learning) model can be used to do supervised classification and regression. This tutorial covers usage of H2O from R. A python version of this tutorial will be available as well in a separate document. This file is available in plain R, R markdown and regular markdown formats, and the plots are available as PDF files. More examples and explanations can be found in our [H2O Deep Learning booklet](http://h2o.ai/resources/) and on our [H2O Github Repository](http://github.com/h2oai/h2o-3/). The PDF slide deck can be found [on Github](https://github.com/h2oai/h2o-world-2015-training/raw/master/tutorials/deeplearning/deeplearning.pdf).
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First, set the path to the directory in which the tutorial is located on the server that runs H2O (here, locally):
2020

‎tutorials/deeplearning/deeplearning.R‎

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#* Deep Learning Tips & Tricks
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#
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### Introduction
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#This tutorial shows how a H2O [Deep Learning](http://en.wikipedia.org/wiki/Deep_learning) model can be used to do supervised classification and regression. This tutorial covers usage of H2O from R. A python version of this tutorial will be available as well in a separate document. This file is available in plain R, R markdown and regular markdown formats, and the plots are available as PDF files. More examples and explanations can be found in our [H2O Deep Learning booklet](http://h2o.ai/resources/) and on our [H2O Github Repository](http://github.com/h2oai/h2o-3/).
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#This tutorial shows how a H2O [Deep Learning](http://en.wikipedia.org/wiki/Deep_learning) model can be used to do supervised classification and regression. This tutorial covers usage of H2O from R. A python version of this tutorial will be available as well in a separate document. This file is available in plain R, R markdown and regular markdown formats, and the plots are available as PDF files. More examples and explanations can be found in our [H2O Deep Learning booklet](http://h2o.ai/resources/) and on our [H2O Github Repository](http://github.com/h2oai/h2o-3/). The PDF slide deck can be found [on Github](https://github.com/h2oai/h2o-world-2015-training/raw/master/tutorials/deeplearning/deeplearning.pdf).
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#
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#First, set the path to the directory in which the tutorial is located on the server that runs H2O (here, locally):
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#

‎tutorials/deeplearning/deeplearning.Rmd‎

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* Deep Learning Tips & Tricks
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## Introduction
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This tutorial shows how a H2O [Deep Learning](http://en.wikipedia.org/wiki/Deep_learning) model can be used to do supervised classification and regression. This tutorial covers usage of H2O from R. A python version of this tutorial will be available as well in a separate document. This file is available in plain R, R markdown and regular markdown formats, and the plots are available as PDF files. More examples and explanations can be found in our [H2O Deep Learning booklet](http://h2o.ai/resources/) and on our [H2O Github Repository](http://github.com/h2oai/h2o-3/).
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This tutorial shows how a H2O [Deep Learning](http://en.wikipedia.org/wiki/Deep_learning) model can be used to do supervised classification and regression. This tutorial covers usage of H2O from R. A python version of this tutorial will be available as well in a separate document. This file is available in plain R, R markdown and regular markdown formats, and the plots are available as PDF files. More examples and explanations can be found in our [H2O Deep Learning booklet](http://h2o.ai/resources/) and on our [H2O Github Repository](http://github.com/h2oai/h2o-3/). The PDF slide deck can be found [on Github](https://github.com/h2oai/h2o-world-2015-training/raw/master/tutorials/deeplearning/deeplearning.pdf).
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‎tutorials/deeplearning/deeplearning.md‎

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* Deep Learning Tips & Tricks
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## Introduction
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This tutorial shows how a H2O [Deep Learning](http://en.wikipedia.org/wiki/Deep_learning) model can be used to do supervised classification and regression. This tutorial covers usage of H2O from R. A python version of this tutorial will be available as well in a separate document. This file is available in plain R, R markdown and regular markdown formats, and the plots are available as PDF files. More examples and explanations can be found in our [H2O Deep Learning booklet](http://h2o.ai/resources/) and on our [H2O Github Repository](http://github.com/h2oai/h2o-3/).
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This tutorial shows how a H2O [Deep Learning](http://en.wikipedia.org/wiki/Deep_learning) model can be used to do supervised classification and regression. This tutorial covers usage of H2O from R. A python version of this tutorial will be available as well in a separate document. This file is available in plain R, R markdown and regular markdown formats, and the plots are available as PDF files. More examples and explanations can be found in our [H2O Deep Learning booklet](http://h2o.ai/resources/) and on our [H2O Github Repository](http://github.com/h2oai/h2o-3/). The PDF slide deck can be found [on Github](https://github.com/h2oai/h2o-world-2015-training/raw/master/tutorials/deeplearning/deeplearning.pdf).
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‎tutorials/intro-to-r-python-flow/intro-to-r.md.R‎

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# Predicting Airline Delays with H2O in R
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### Load the H2O R package and start an local H2O cluster
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###### Connection to an H2O cloud is established through the `h2o.init` function from the `h2o` package.
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###### When starting H2O from R, specify `nthreads` equal to -1, in order to utilize all the cores on your machine.
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###### To connect to a pre-existing H2O cluster make sure to edit the H2O location with argument `myIP` and `myPort`.
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library(h2o)
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h2o.init(nthreads = -1)
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### Import Data into H2O
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###### We will use the `h2o.importFile` function to do a parallel read of the data into the H2O distributed key-value store.
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###### During import of the data, features Year, Month, DayOfWeek, and FlightNum were set to be parsed as enumerator or categorical rather than numeric columns.
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airlines.hex <- h2o.uploadFile(path = normalizePath("../data/allyears2k.csv"), destination_frame = "allyears2k.hex")
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###### Get an overview of the airlines dataset quickly by running `summary`.
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summary(airlines.hex)
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### Building a GLM Model
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###### Run a logistic regression model using function `h2o.glm` and selecting “binomial” for parameter `Family`.
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###### Add some regularization by setting alpha to 0.5 and lambda to 1e-05.
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y <- "IsDepDelayed"
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x <- c("Dest", "Origin", "DayofMonth", "Year", "UniqueCarrier", "DayOfWeek", "Month", "Distance")
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glm_model <- h2o.glm(x = x, y = y, training_frame = airlines.hex, model_id = "glm_model",
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solver = "IRLSM", standardize = T, link = "logit",
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family = "binomial", alpha = 0.5, lambda = 1e-05)
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auc <- h2o.auc(object = glm_model)
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print(paste0("AUC of the training set : ", round(auc, 4)))
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print(glm_model@model$standardized_coefficient_magnitudes)
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print(glm_model@model$scoring_history)
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### Building a Deep Learning Model
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###### Build a binary classfication model using function `h2o.deeplearning` and selecting “bernoulli” for parameter `Distribution`.
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###### Run 100 passes over the data by setting parameter `epoch` to 100.
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dl_model <- h2o.deeplearning(x = x, y = y, training_frame = airlines.hex, distribution = "bernoulli", model_id = "deeplearning_model2",
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epochs = 100, target_ratio_comm_to_comp = 0.02, seed = 6765686131094811000, variable_importances = T)
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auc2 <- h2o.auc(object = dl_model)
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print(paste0("AUC of the training set : ", round(auc2, 4)))
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print(h2o.varimp(dl_model))
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print(h2o.scoreHistory(dl_model))
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‎tutorials/model-selection/README.md‎

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