Using AutoML to evaluate tha hyperparameters of the algorithm Word2Vec

Question

Is it possible with AutoML (from H2O) to use only the Word2Vec algorithm and try out different values for the parameters to find out which parameter settings give me the most accurate vectors for my data set? So I don't want AutoML to apply the algorithms DeepLearning, GBM etc. to my dataset. Only the Word2Vec algorithm… How Do I do that?

So far I only managed to build a word2vec model with H2O.

I would like to test different Settings of the hyperparameters of Word2Vec with AutoML to evaluate which Settings are optimal...

AutoML clearly has exclude_algos parameter: docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html Also have a look docs.h2o.ai/h2o/latest-stable/h2o-docs/grid-search.html for Hyperparameter optimization in h2o — Jav

Erin LeDell Erin LeDell · Accepted Answer · 2019-03-26T16:54:05

The Word2Vec algorithm is a data transformation algorithm (converting rows of text to a matrix), not a supervised machine learning algorithm (which is what AutoML and all the algorithms inside of it do).

The typical way that Word2Vec is used is it apply Word2Vec to your text data so that your data can be used to train a supervised ML algorithm. From here you can run any supervised algorithm (GLM, Random Forest, GBM, etc) on this transformed dataset -- or my recommendation is to just pass the transformed data to AutoML, so it can find the best algorithm for you.

You will have to try out different settings for Word2Vec manually and see how well they do, given some particular supervised learning algorithm that you want to apply to your problem. Hopefully that clears up the confusion.

Using AutoML to evaluate tha hyperparameters of the algorithm Word2Vec

1 Answers