Deep Learning with H2O in Python
Step 1- First of all , we need to install H2o package in Python. on anaconda prompt pip install h2o Step 2- Initialize and start the cluster - h2o . init () from h2o.estimators.deeplearning import H2ODeepLearningEstimator Step 3- load train and test data set- train = h2o . import_file ( "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv" ) Step 4- Creating test and train data set using split- splits = train . split_frame ( ratios = [ 0.75 ], seed = 1234 ) Step 5- Configuring the model- model = H2ODeepLearningEstimator(distribution = "AUTO",activation = "RectifierWithDropout",hidden = [32,32],input_dropout_ratio = 0.2,l1 = 1e-5,epochs = 10) Step 6- train(fit the model)- model.train(x="sepal_len", y=["petal_len"], training_frame=splits[0]) Step 7- predicting using trained model and creating a new column in test data- (splits[1]['predicted_sepal_len'])=...