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Chapter 4
The PCA/Factor node provides powerful data-reduction techniques to reduce
the complexity of your data. Principal components analysis (PCA) finds linear
combinations of the input fields that do the best job of capturing the variance in the
entire set of fields, where the components are orthogonal (perpendicular) to each
other. Factor analysis attempts to identify underlying factors that explain the pattern
of correlations within a set of observed fields. For both approaches, the goal is to
find a small number of derived fields that effectively summarizes the information in
the original set of fields.
The Feature Selection node screens input fields for removal based on a set of criteria
(such asthe percen tageof missin gvalu es);it then ranks the importance of remaining
inputs relative to a specified target. For example, given a data set with hundreds of
potential inputs, which are most likely to be useful in modeling patient outcomes?
Discriminant analysis makes more stringent assumptions than logistic regression but
can be a valuable alternative or supplement to a logistic regression analysis when
those assumptions are met.
Logistic regression is a statistical technique for classifying records based on values
of input fields. It is analogous to linear regression but takes a categorical target field
instead of a numeric range.
The Generalized Linear model expands the general linear model so that the
dependent variable is linearly related to the factors and covariates through a specified
link function. Moreover, the model allows for the dependent variable to have a
non-normal distribution. It covers the functionality of a wide number of statistical
models, including linear regression, logistic regression, loglinear models for count
data, and interval-censored survival models.
Ageneralized line armix ed model (GLMM) extends the linear model so that the target
canhave a non -normal distribution, is linearly related to the factors and covariates via
a specifiedlin k function, and so that the observations can be correlated. Generalized
linearm ixed models cover a wide variety of models, from simple linear regression to
complex multilevel models for non-normal longitudinal data.
TheC ox regression node enables you to build a survival model for time-to-event data
in the presence of censored records. The model produces a survival function that
predicts the probability that the event of interest has occurred at a given time (t)
for given values of the input variables.
The Support VectorMachine (SVM) node enabl es you to classify data into one of
two groups without overfitting. SVM works well with wide data sets, such astho se
with a very large number of input fields.
The Bayesian Network node enables you to build a probability model by combining
observedand recor dedev idence with real-world knowledge to establish the likelihood
of occurrences. The node focuses on Tree Augmented Naïve Bay es (TAN)and
Markov Blanket networks that are primarily used for classification.