H2O Machine Learning AutoML Roadmap 2016.10
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Brief presentation that lays out the landscape and current roadmap for automatic or guided semi-automatic #machinelearning in H2O. Tags: #automl #datascience #bigdata #analytics #ai https://www.youtube.com/watch?v=ZZSe3osXK_E
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H2O Machine Learning AutoML Roadmap 2016.10
- 1. H2O AutoML Roadmap 2016.10 Raymond Peck Director of Product Engineering, H2O.ai rpeck@h2o.ai © H2O.ai, 2016 1
- 2. What Will We Cover? • What is AutoML? • What is the roadmap for H2O AutoML? © H2O.ai, 2016 2
- 3. What is AutoML? H2O AutoML automates parts of data preparation and model training in order to help both Machine Learning / Data Science experts and complete novices. Other AutoML projects concentrate on novices. © H2O.ai, 2016 3
- 4. Outside AutoML Projects • auto-sklearn • AutoCompete • TPOT • DataRobot • Automatic Statistician • BigML • et al... © H2O.ai, 2016 4
- 5. Who is the Target Audience? • "Big green button" for novice users such as software developers and business analysts; • Iterative, interactive use and controls for expert users: • Machine Learning experts • Descriptive Data Scientists © H2O.ai, 2016 5
- 6. What Are the Pieces? • data cleaning • feature engineering / feature generation • feature selection • for both the original and generated features • model hyperparameter tuning • automatic smart ensemble generation © H2O.ai, 2016 6
- 7. Prior Work @ H2O • ensembles (stacking), from Erin LeDell • random hyperparameter search with automatic stopping, from Raymond Peck • some dataset characterization and feature engineering, from Spencer Aiello • hyperopt Bayesian hyperparameter optimization, from Abhishek Malali © H2O.ai, 2016 7
- 8. Current Work • random hyperparameter search with parameter values based on open datasets • moving ensembles into the back end • working on basic metalearning for hyperparameter vectors, starting with 140 OpenML datasets © H2O.ai, 2016 8
- 9. Future Work • feature selection • feature engineering for IID data • Bayesian hyperparameter search with warm start • feature engineering for non-IID data, e.g. time series • iterate w/ larger datasets that are typical for our customers • distribution guesser for regression © H2O.ai, 2016 9
- 10. How Do We Evaluate Our Work? • public datasets from • OpenML • ChaLearn AutoML challenge • Kaggle • our own Data Scientists' work with customer datasets • customer feedback (soon) © H2O.ai, 2016 10
- 11. Data Cleaning • outlier analysis (with user feedback) • sentinel value detection • as a side-effect of outlier analysis • type-based heuristics (e.g., 999999, 1970.01.01) • identifier detection (e.g., customer ID) • smart imputation © H2O.ai, 2016 11
- 12. Feature Generation We will be using several techniques including: • type-based heuristics • date/time expansion • log and other transforms of numerics • interactions (product, ratio, etc) • feature generation with Deep Learning deepfeatures() • clustering © H2O.ai, 2016 12
- 13. Feature Selection We will be evaluating several techniques including: • Mutual Information (non-linear correlation) • variable importance from GBM and Deep Learning • PCA • GLM with Elastic Net / LASSO Perhaps different selectors for initial data and transforms / interactions to trade off speed and the detection of non-linear relationships. © H2O.ai, 2016 13
- 14. Hyperparameter Tuning • currently do random hyperparameter search with metric-based smart stopping • hyperparameter values taken from hand-tuning 140 OpenML datasets • soon adding simple "nearest neighbors" warm start (basic metalearning) • then adding Bayesian hyperparameter optimization • possibly integrating hyperopt into the back end © H2O.ai, 2016 14
- 15. Automatic Smart Ensemble Generation • currently adding Erin LeDell's stacking / SuperLearner into the back end • initially, ensemble top N models from hyperparameter searches • optional "use original features" • smarter ensemble generation for faster scoring, less overfitting: • greedy ensemble creation • ensemble models with uncorrelated residuals © H2O.ai, 2016 15
- 16. Possible Futures • multiple concurrent H2O clusters for speed • freeze/thaw model training • outlier analysis with user feedback • residuals analysis with user feedback • composite models using pre-clustering step • try to predict accuracy from dataset metadata • training time prediction • scoring time prediction © H2O.ai, 2016 16