H2O Machine Learning with KNIME Analytics Platform - Christian Dietz - H2O AI World London

Editor's Notes

1. this competition was published by a japanese Restaurant chain. They wanted to know the number of future visitors for their different stores Lets see what kind of data they provided us to solve this problem
2. This is the top-level-workflow we used to solve the problem It will guide us through the major steps from reading in the data up to doing the prediction and showcases the interaction of the knime native and the h2o nodes
3. Lets jump right into our data preparation
4. Data preparation part of the workflow We‘ll not discuss it in too much detail
5. In the end we get two datasets the trainset with information about the number of visitors (target variable), which we will use to build our model in the next steps the test dataset without the number of visitors. These have to be predicted by our model and submitted to kaggle lateron
6. We just did the data preparation, before we jump right into the modeling we have to create a local h2o context and convert our knime table into an h2o frame This frame will be used to build our models
7. At the moment there are three H2O models implemented in KNIME which are capable of solving such a regression task: Random Forest, Generalized Linear Model and Gradient Boosting Machine Lets have a look at one of those to see how we trained, optimized and evaluated our models
8. The actual learning of a model happens in one single node: The H2O Random Forest Learner takes the h2o frame with the testset and builds a model Configuration dialog: What is the target variable you want to predict? Here it is visitors, enter some model specific parameters, e.g. number of levels of a single tree and the number of tree models in this forest Next we use the H2O predictor to use the just created model to predict the visitors for our testset Afterwards the score of the model is computed with the H2O regression scorer. as performance measure we used the root mean squared logarithmic error, as this measure is also used on Kaggle to evaluate the final submissions.
9. To avoid overfitting we use the h2o cross validation loop, which partitions the data and trains one model for each partition of the data !!! Tabelle mit mean von cv einbauen !!!
10. With one machine learning algorithm, here e.g. random forest, you can solve different problems. With parameters, for a random forest e.g. the number of trees and the treedepth, one can adapt it to a specific problem with respect to the objective function. Here we are looking for parameters that minimize the error of our model validations We did it with a grid search that performs one iteration of the loop for every possible combination of parameters At the end we have a table with all parameter combinations and their respective scores
11. At the end of the loop we’ve got all parameter combinations with their respective scores. We selected the parameters that lead to the best result and trained a new model on the complete public dataset As you can see we’ve got a nested loop here. Luckily the new H2O nodes are really fast, so this is not gonna be a performance issue
12. The steps I just showed you happen in all three nodes. Afterwards we select the model which scored best
13. convert it into an h2o MOJO, which is a model object that is optimized to be embedded in any java environment By doing this we are able to use our just created model outside of an H2O context. We can for example do our prediction for the submission dataset from Kaggle Or we can deploy it to where ever we want, so we just stored it somewhere for Christian. Lets see he is doing with it.