H2O machine learning algorithms
H2O has extensive unsupervised and supervised learning algorithms with similar reusable API constructs – for example, similar ways to set hyperparameters or invoke explainability capabilities. These algorithms are identical from an H2O 3 or Sparkling Water perspective and are overviewed in the following diagram:
Figure 4.2 – H2O algorithms
Each algorithm has an extensive set of parameters and hyperparameters to set or leverage as defaults. The algorithms accept H2OFrames as data inputs. Remember that an H2OFrame is simply a handle on the IDE client to the distributed in-memory data on the remote H2O cluster where the algorithm processes it.
Let's take a look at H2O's distributed machine learning algorithms.
H2O unsupervised learning algorithms
Unsupervised algorithms do not predict but rather attempt to find clusters and anomalies in data, or to reduce the dimensionality of a dataset. H2O...
