# H2O Releases 3.40.0.1 and 3.42.0.1

RSS Category: H2O, H2O Release, H2O-3, Open Source

Our new major releases of H2O are packed with new features and fixes! Some of the major highlights of these releases are the new Decision Tree algorithm, the added ability to grid over Infogram, an upgrade to the version of XGBoost and an improvement to its speed, the completion of the maximum likelihood dispersion parameter and its expansion to the Negative Binomial family, and many more exciting features!

### Decision Tree

We implemented the new Decision Tree algorithm which is a powerful tool for classification and regression tasks. The Decision Tree algorithm creates a tree structure in which each internal node represents a test on one attribute. Each branch emerging from a node represents an outcome of the test, and each lead node represents a class label or a predicted value. The Decision Tree algorithm follows a recursive process to build the tree structure. This implementation currently only supports numeric features and a binary target variable.

#### Binning for Tree Building

To handle large datasets efficiently, the Decision Tree algorithm utilizes binning as a preprocessing step at each internal node. Binning involves discretizing continuous features into a finite number of bins. This reduces the computational complexity of finding the best attribute and threshold for each split. The binned features are then used for split point selection during tree construction, allowing for faster computation. The attribute and threshold combination that minimizes the weighted average of the entropies of the resulting subsets is selected as the best split point.

#### Entropy as a Splitting Rule

The Decision Tree algorithm employs entropy as the splitting rule to determine the best attribute and threshold for each node. Entropy measures the impurity or disorder within a set of samples. The goal is to find splits that minimize the entropy and create homogenous subsets with respect to the target variable.

The entropy of a set `S` with respect to a binary target variable can be calculated using the following formula:

where:

• p1 is the proportion of positive (or class 1) samples in `S`

• p0 is the proportion of negative (or class 0) samples in `S`

The attribute and threshold combination that minimizes the weighted average of the entropies of the resulting subsets is selected as the best split point.

### GLM AIC and log likelihood implementation

We have implemented the calculation of full log likelihood and full Akaike Information Criterion (AIC) for the following Generalized Linear Models (GLM) families: Gaussian, Binomial, Quasibinomial, Fractional Binomial, Poisson, Negative Binomial, Gamma, Tweedie, and Multinomial.

The log likelihood is computed using specific formulas tailored to each GLM family, while the AIC is calculated using a common formula that utilizes the calculated log likelihood.

To manage the computational intensity of the implementation, we introduced the `calc_like` parameter. Setting `calc_like=True`, you enable the calculation of log likelihood and AIC. This computation is performed during the final scoring phase after the model has been built.

Consider the following:

• For the Gaussian, Gamma, Negative Binomial, and Tweedie families, it is necessary to estimate the dispersion parameter. During initialization, the `compute_p_values` and `remove_collinear_columns` parameters are automatically set to `True` to facilitate the estimation process. For the Gaussian family, the `dispersion_parameter_method` parameter is set to `"pearson"` and for the Gamma, Negative Binomial, and Tweedie families, the `dispersion_parameter_method` is set to `"ml"`.

• The log likelihood value is not available in the cross-validation metrics. The AIC, however, is available and is calculated by the original simplified formula independent of the log likelihood.

### Maximum likelihood estimation of dispersion parameter estimation for Negative Binomial GLM

We implemented negative binomial regression with dispersion parameter estimation using the maximum likelihood method for Generalized Linear Models (GLM). Regularization is not supported when using dispersion parameter estimation that uses the maximum likelihood method. To use this new feature, set the `dispersion_parameter_method="ml"` along with `family="negativebinomial"` in the GLM constructor.

### Variance power and dispersion estimation for Tweedie GLM

We implemented maximum likelihood estimation for Tweedie variance power in GLM. Regularization is not supported when using the maximum likelihood method. To use this new feature, set the `dispersion_parameter_method="ml"` along with `family="tweedie"`, `fix_dispersion_parameter=True`, and `fix_tweedie_variance_power=False` in the GLM constructor. Use `init_dispersion_parameter` to specify the dispersion parameter (ɸ) and `tweedie_variance_power` to specify the initial variance power to start the estimation at.

To estimate both Tweedie variance power and dispersion, set `dispersion_parameter_method="ml"` along with `family="tweedie"`, `fix_dispersion_parameter=False`, and `fix_tweedie_variance_power=False` in the GLM constructor. Again, use `init_dispersion_parameter` to specify the dispersion parameter (ɸ) and `tweedie_variance_power` to specify the initial variance power to start the estimation at.

For datasets containing zeroes, the Tweedie variance power is limited to (1,2). Likelihood of the Tweedie distribution with a variance power close to 1 is multimodal, so the likelihood estimation can end up in a local optimum.

For Tweedie variance power and dispersion estimations, estimation is done using the Nelder-Mead algorithm and has similar limitations to Tweedie variance power.

If you believe the estimate is a local optimum, you might want to increase the `dispersion_learning_rate`. This only applies to Tweedie variance power and dispersion estimation.

### Regression Influence Diagnostic

We implemented the Regression Influence Diagnostic (RID) for the Gaussian and Binomial families for GLM. This implementation finds the influence of each data row on the GLM coefficients’ values for the IRLSM solver. RID determines the coefficient change for each predictor when a data row is included and excluded in the dataset used to train the GLM model.

For the Gaussian family, we were able to calculate the exact RID. For the Binomial family, we use an approximation formula to determine the RID.

### Interaction column support in CoxPH MOJO

Cox Proportional Hazards (CoxPH) MOJO now supports all interaction columns (i.e. `enum` to `enum`, `num` to `enum`, and `num` to `num` interactions).

### Improved GAM tutorial

We improved the Generalized Additive Models (GAM) tutorial to make it more user-friendly by employing cognitive load theory principles. This change allows you to concentrate on a single concept for each instruction which reduces your cognitive strain and will help to improve your comprehension.

### Grid over Infogram

As a continuation of the Admissible Machine Learning (ML), we implemented a simple way to train models on features ranked using Infogram. This eliminates the need to specify some threshold value.

After training those models, you need a way to select the best one. To do so, we implemented the calculation of common metrics on the individual intersections. These metrics are then aggregated to form an extended leaderboard. The extended leaderboard can be used for model selection since, in cases like these, you would want to optimize by model performance and model fairness. You can use Pareto front (`h2o.explanation.pareto_front` / `h2o.pareto_front`) to do that. This command results in a plot and a subset of the leaderboard frame containing the Pareto front.

Once you pick a promising model, you can use `<model_name>.inspect_model_fairness` / `h2o.inspect_model_fairness(<model_name>)` to look at those metrics calculated on individual intersections. These include common performance metrics such as AUC, AUCPR, F1, etc., adverse impact ratio on those metrics, ROC, Precision-Recall Curve per intersection, PDP per intersection, and, if available (i.e. the model is a tree-based model), SHAP contributions per intersection. For more details, refer to the user guide.

### Upgrade to XGBoost 1.6

The transition from XGBoost version 1.2 to 1.6 in the H2O-3 platform marks a major milestone in the evolution of this widely used algorithm. XGBoost, renowned for its efficiency and accuracy in handling structured datasets, has been a go-to choice for many data scientists. With the upgrade to version 1.6, H2O-3 raises the bar even further, providing users with an array of enhanced features and improvements.

One notable highlight of XGBoost 1.6 is its boosted performance, thanks to optimized algorithms and implementation. The upgrade includes various efficiency enhancements, such as improved parallelization strategies, memory management, and algorithmic tweaks. These improvements translate into faster training times and more efficient memory utilization, allowing you to process larger datasets and experiment with complex models more efficiently.

### MOJO support for H2OAssembly

H2OAssembly is part of the H2O-3 API that enables you to form a pipeline of data munging operations. The new version of the class introduces the `download_mojo` method that converts an H2OAssembly pipeline to the MOJO2 artifact that is well-known from DriverlessAI. The conversion currently supports the following transformation stages:

### GBM interpretability

We brought another insight into H2O’s Gradient Boosting Machines (GBM) algorithm. This enhancement is the ability to retrieve row-to-tree assignments directly from the algorithm. This addresses the challenge of understanding how individual data points are assigned to specific decision trees within the ensemble. This new feature allows you to gain deeper insights into the decision-making process, thus enabling greater transparency and understanding of GBM models.

### GBM Poisson distribution deviance

We have updated the deviance calculation formula for the Poisson family in GBM. To ensure accurate and reliable results, we introduced a new formula:

which replaces the previously used formula:

This previous formula, though optimized and maintaining the deviance function behavior, produced incorrect output values. No longer! To validate the correctness of the new formula, we compared it with the deviance calculations in scikit-learn.

### End of support for Python 2.7 and 3.5

Support for Python 2.7 and 3.5 have been removed from the project to get rid of vulnerabilities connected with the future package. If you need to use Python 2.7 to 3.5, please contact sales@h2o.ai.

### Documentation improvements

Parameters for all supervised and unsupervised algorithms have been standardized, updated, and reordered to help you more easily find the information you need. Each section has been divided into algorithm-specific parameters and common parameters. GBM, DRF, XGBoost, Uplift DRF, Isolation Forest, and Extended Isolation Forest have an additional “shared tree-based algorithm parameters” section. All GLM family parameters have been centralized to the GLM page with icons showing which GLM family algorithm shares that parameter. Autoencoder for Deep Learning and HGLM for GLM also have their own parameter-specific sections.

The grid search hyperparameters have also been updated.

Contributors

Marek Novotný, Wendy Wong, Adam Valenta, Erin LeDell, Tomáš Frýda, Bartosz Krasinski, Yuliia Syzon, Sebastien Poirier, Amin Sedaghat, Hannah Tillman

#### About the Authors

###### Marek Novotny

Marek is a software engineer focusing on development of the Sparkling Water project. He obtained a master degree in computer science at Charles University in Prague. Before Marek joined the team, he spent several years in financial industry developing scalable and fault-tolerant software systems. He is excited about learning new things and open-source software.

###### Wendy Wong

Wendy is a hacker at H2O.ai devising solutions to making systems smarter. Prior to working at H2O.ai, she was building intelligent applications on mobile devices to recognize user activities from sensor data and predict user app usage from user logs at Lab126. At Intel Labs and Aperto Networks, she was a system engineer/architect designing wireless communication systems for WiFi and cellular networks. Wendy obtained her bachelor in electrical engineering from Purdue University and a master and Ph.D. in Electrical Engineering from Cornell. She loves machine learning, swarm intelligence, mathematics and wireless communication systems. She enjoys being involved with all phases of the product development cycle; requirements analysis (what are we building and how well does it need to perform), architecture/algorithm design, performance analysis and simulation, prototyping/implementation, integration, test, and verification. Her diverse interests and skills are reflected in her patents. In her spare time, Wendy loves learning new things, being active, reading, and scuba diving. She loves the ocean and wishes she could be an amphibian someday.

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