H2O PCA

PCA is commonly used to model without regularization or perform dimensionality reduction. It can also be useful to carry out as a preprocessing step before distance-based algorithms such as K-Means since PCA guarantees that all dimensions of a manifold are orthogonal.

Input

It takes in a DataFrame as input

Type

ml-estimator

Class

fire.nodes.h2o.NodeH2OPCA

Fields

Name	Title	Description
transform	Transform	Transformation of training data.
pcaMethod	PCA Method	Specify the algorithm to use for computing the principal components: GramSVD - uses a distributed computation of the Gram matrix, followed by a local SVD; Power - computes the SVD using the power iteration method (experimental); Randomized - uses randomized subspace iteration method; GLRM - fits a generalized low-rank model with L2 loss function and no regularization and solves for the SVD using local matrix algebra (experimental).
pcaImpl	PCA Implementation	Specify the implementation to use for computing PCA (via SVD or EVD): MTJ_EVD_DENSEMATRIX - eigenvalue decompositions for dense matrix using MTJ; MTJ_EVD_SYMMMATRIX - eigenvalue decompositions for symmetric matrix using MTJ; MTJ_SVD_DENSEMATRIX - singular-value decompositions for dense matrix using MTJ; JAMA - eigenvalue decompositions for dense matrix using JAMA. References: JAMA - http://math.nist.gov/javanumerics/jama/; MTJ - https://github.com/fommil/matrix-toolkits-java/.
splitRatio	Split Ratio	Split Ratio
k	K	Rank of matrix approximation.
maxIterations	Max Iterations	Maximum training iterations.
seed	Seed	Seed for pseudo random number generator (if applicable).
useAllFactorLevels	Use All Factor Levels	Whether first factor level is included in each categorical expansion.
computeMetrics	Compute Metrics	Whether to compute metrics on the training data.
imputeMissing	Impute Missing	Whether to impute missing entries with the column mean.
ignoreConstCols	Ignore Const Columns	Ignore constant columns.
scoreEachIteration	Score Each Iteration	Whether to score during each iteration of model training.
maxRuntimeSecs	Max Runtime Secs	his argument specifies the maximum time that the AutoML process will run for. If both max_runtime_secs and max_models are specified, then the AutoML run will stop as soon as it hits either of these limits. If neither max_runtime_secs nor max_models are specified, then max_runtime_secs defaults to 3600 seconds (1 hour).
ignoredCols	Ignored Columns	Features to be ignored for Modelling
columnsToCategorical	Columns to Categorical	Columns to be Categorical encoded

Details

Principal Components Analysis (PCA) is closely related to Principal Components Regression. The algorithm is carried out on a set of possibly collinear features and performs a transformation to produce a new set of uncorrelated features.

More details are available at : http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/pca.html