H2O Machine Learning Cheat Sheet
An H2O machine learning reference for data import, train-validation splits, algorithms, metrics, grid search and model interpretation.
What this reference covers
The H2O reference follows a reproducible experiment from data-frame import through train, validation and test splits, algorithm selection, metrics, grid search, model saving and result review. It is a workflow index rather than a substitute for feature engineering or domain validation.
Protect the evaluation boundary
Define the target and feature columns, task type, time range and missing-value rules before training. Fit preprocessing, feature selection and imputation inside the training boundary. A test set that has influenced tuning no longer provides an independent estimate.
Compare models with more than one number
Start with a simple baseline, then record algorithm, parameters, metric, runtime, resource use, random seed and error examples. Use an untouched test set for the final report and inspect confusion matrices, residuals or representative mistakes.
Maintenance note
A high score does not prove business suitability. Bias, drift, class imbalance, privacy and access controls need separate review before a model is used beyond an experiment. Content review date: 2026-08-23.
Save to your cloud drive
Save the complete collection first so files remain together and are easier to access across devices.
Quark Cloud Drive
RecommendedSave H2O Machine Learning Cheat Sheet to this cloud drive
Baidu Netdisk
Save H2O Machine Learning Cheat Sheet to this cloud drive
H2O machine learning study guide
Fix the target and split rules first, establish a baseline, compare models on an independent test set and record both quality and resource evidence.
Before you start
- Prepare a PDF reader and a small, non-sensitive data set.
- Understand training, validation and test sets, feature columns and target columns.
- Choose a task type and evaluation metric before looking at model results.
Quick start
- 01
Define the task and data boundary
Separate classification, regression and clustering, and specify target, features, missing values and time range before training.
- 02
Fix the data split
Assign clear roles to train, validation and test data and keep the same split rule when comparing experiments.
- 03
Train a baseline
Use a simple model to verify the data pipeline, then try additional algorithms while recording parameters and resource use.
- 04
Evaluate independently
Choose task-appropriate metrics, report once on untouched test data and inspect error examples, residuals or confusion matrices.
Usage tips
- Fit preprocessing, feature selection and imputation only inside the training boundary.
- Record metric, runtime, resource cost and interpretability when comparing models.
- Preserve data split, parameters, random seed and model version for reproduction.
Troubleshooting and uninstall
Why is validation performance high but test performance poor?
Check leakage, duplicate records, split rules, repeated tuning and indirect use of the test set, then return to a simple baseline.
Why is training using too many resources?
Reduce feature or model size, limit parallelism or validate on a small sample before expanding the experiment to the full budget.
Frequently asked questions
Who is the H2O cheat sheet for?
It is for R or machine learning learners who want a quick path through H2O data import, training, evaluation and tuning.
Why not rely on training metrics alone?
Training metrics can be inflated by overfitting, so independent validation or test data and error inspection are needed.
Where does data leakage commonly happen?
Common sources include fitting preprocessing on all data, using future fields, duplicate rows across splits and tuning repeatedly on the test set.
What should be recorded for model comparison?
Record the split, algorithm, parameters, metrics, runtime, resource use, random seed and independent test result.