h2o

I am trying to initialize H2O from Python. I am using python 2.7.9. I followed the steps below to get h2o python module: pip install requests pip install tabulate # Remove any preexisiting H2O module. pip uninstall h2o # Next, use pip to install this version of the H2O Python module. pip install http://h2o-release.s3.amazonaws.com/h2o-dev/master/1109/Python/h2o-0.3.0.1109-py2.py3-none-any.whl I get this error when I call h2o.init(). No instance found at ip and port: localhost:54321. Trying to start local jar... No jar file found. Could not start local instance. Traceback (most recent call last

I tried to use H2O to create some machine learning models for binary classification problem, and the test results are pretty good. But then I checked and found something weird. I tried to print the prediction of the model for the test set out of curiosity. And I found out that my model actually predicts 0 (negative) all the time, but the AUC is around 0.65, and precision is not 0.0. Then I tried to use Scikit-learn just to compare the metrics scores, and (as expected) they’re different. The Scikit learn yielded 0.0 precision and 0.5 AUC score, which I think is correct. Here's the code that I

I try to load table into R using h2o but had the following error my_data <- h2o.import_sql_table(my_sql_conn, table, username, password) ERROR: Unexpected HTTP Status code: 500 Server Error (url = http://localhost:54321/99/ImportSQLTable ) java.lang.RuntimeException [1] "java.lang.RuntimeException: SQLException: No suitable driver found for jdbc:mysql://10.140.20.29/MySQL?&useSSL=false\nFailed to connect and read from SQL database with connection_url: jdbc:mysql://10.140.20.29/MySQL?&useSSL=false" Can someone help me with this? Thank you so much! You need a supported JDBC (Build on JDBC 42

Accoding to the Description of function: h2o.stack: This function creates a "Super Learner" (stacking) ensemble using a list of existing H2O base models specified by the user. h2o.ensemble: This function creates a "Super Learner" (stacking) ensemble using the H2O base learning algorithms specified by the user. They are two different ways to construct an ensemble. They have a different interface, but they produce the exact same type of object in the end. The h2o.stack() function takes as input a list of already trained (and cross-validated) H2O models, so all it needs to do is the metalearning

I'm trying a random forest classification model by using H2O library inside R on a training set having 70 million rows and 25 numeric features.The total file size is 5.6 GB. The validation file's size is 1 GB. I have 16 GB RAM and 8 core CPU on my system. The system successfully able to read both of the files in H2O object. Then I'm giving below command to build the model: model <- h2o.randomForest(x = c(1:18,20:25), y = 19, training_frame = traindata, validation_frame = testdata, ntrees = 150, mtries = 6) But after few minutes (without generating any tree), I'm getting following error: "Error