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Logistic Regression With H2O Deep Learning in Scala [Code Snippet]

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Logistic Regression With H2O Deep Learning in Scala [Code Snippet]

In this heavily code-based tutorial, learn how to perform logistic regression using feed-forward network-based deep learning algorithms and H2O.

· AI Zone ·
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Here is some sample code that shows an example of using feed-forward network-based deep learning algorithms with H2O to perform logistic regression.

First, let's import key classes specific to H2O:

import org.apache.spark.h2o._
import water.Key
import java.io.File

Now, we will create H2O context so that we can call key H2O functions specific to data ingest and deep learning algorithms:

val h2oContext = H2OContext.getOrCreate(sc)
import h2oContext._
import h2oContext.implicits._

Let's import data from a local file system as an H2O DataFrame:

val prostateData = new H2OFrame(new File("/Users/avkashchauhan/src/github.com/h2oai/sparkling-water/examples/smalldata/prostate.csv"))

Next, let's import deep learning classes:

import root.hex.deeplearning.DeepLearning
import root.hex.deeplearning.DeepLearningModel.DeepLearningParameters

Now, we will define all key parameters specific to the H2O deep learning algorithm:

val dlParams = new DeepLearningParameters()
dlParams._epochs = 100
dlParams._train = prostateData
dlParams._response_column = 'CAPSULE
dlParams._variable_importances = true
dlParams._nfolds = 5
dlParams._seed = 1111
dlParams._keep_cross_validation_predictions = true;

Now we will create the Deep Learning Algorithm key first and then start the deep learning algorithm in blocking mode:

val dl = new DeepLearning(dlParams, Key.make("dlProstateModel.hex"))
val dlModel = dl.trainModel.get()

Let's learn more about our model with dlModel.

Now, we can perform the prediction by passing an H2O DataFrame. Here, I am simply passing the original DataFrame. However, you can load your test DataFrame and pass it as an H2O frame to perform prediction:

val predictionH2OFrame = dlModel.score(prostateData)('predict)
val predictionsFromModel = asRDD[DoubleHolder](predictionH2OFrame).collect.map(_.result.getOrElse(Double.NaN))

That's it — enjoy!

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Topics:
h2o ,machine learning ,regression model ,deep learning ,predictive analytics ,ai

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