How to

# Getting Started

From downloading through to building your first workflow

**1 903**
results

- Train a classification model using the Decision Tree algorithm. Evaluate the accuracy of the class prediction by scoring metrics, ROC Curve, and Lift Chart.
- Train a classification model using the Decision Tree algorithm. Evaluate the accuracy of the class prediction by scoring metrics, ROC Curve, and Lift Chart.
- Train a classification model using the Decision Tree algorithm. Evaluate the accuracy of the class prediction by scoring metrics, ROC Curve, and Lift Chart.
- This example shows how to build an H2O GLM model for regression, predict new data and score the regression metrics for model evaluation.
- This example shows how to evaluate the performance of H2O classification (binominal and multinominal) and regression models.
- This example shows how to build an H2O GLM model for regression, predict new data and score the regression metrics for model evaluation.
- This workflows shows how to train a model for named-entity recognition. The model can be created with the StanfordNLP NE Learner node which creates a conditional random field (CRF) model. To create th…
- The evaluation of uplift models differs much from other predictive analytics approaches. This node evaluates the quality of an Uplift Model by analyzing the real uplift in given bins compared to the p…Manipulator
- Application that evaluates TPs (true positives), TNs, FPs, and FNs for an idXML file with predicted RTs. Web Documentation for RTEvaluationManipulator
- These workflows demonstrate interesting way to use loops. They replicate functionality that's available as a set of nodes