exercise 6.3
#biological predictors can be used to assess the quality of the raw mateiral before processing.
#manufacturing process predictors can be changed in the manufacturing process.
library(AppliedPredictiveModeling)
data(ChemicalManufacturingProcess)
ChemicalManufacturingProcess
#12 biological predictors, 45 process predictors, 176 manufacturing runs
head(ChemicalManufacturingProcess)
set.seed(1)
trainrows=createDataPartition(ChemicalManufacturingProcess[ ,1], p=0.8, list = FALSE)
trainrows
trainpredictors=ChemicalManufacturingProcess[trainrows, ]
dim(trainpredictors)
testpredictors=ChemicalManufacturingProcess[-trainrows, ]
dim(testpredictors)
Summary of Chapter 7
Nonlinear
Regression Models
Like
PLS, the outcome is modeled by an intermediary set of unobserved variables (hidden
variables or hidden units). These hidden units are linear combinations of the
original predictors, but unlike PLS models, they are not estimated in a
hierarchical fashion.
The
linear combination is typically transformed by a nonlinear function g, such as
the logistic (i.e., sigmoidal) function:
The
total number of parameters is H(P+1)+H+1.
Back-propagation
algorithm is a highly efficient methodology that works with derivatives to find
the optimal parameters. However, it is common that a solution to this equation
is not a global solution.
Weight
decay (moderate over-fitting):
An
alternative version of the sum of the squared errors:
Tomorrow, I will continue to read Chapter 7.
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