#load package
library(gradDescent)
#load data
data("gradDescentRData")
#preprocess data
cf.data <- gradDescent.preprocessing(
gradDescentRData$CompressilbilityFactor,
trainRate=0.8,
normalizationMethod="variance",
seed=1
)
#model building / data learning
GD10.model <- gradDescent.learn(
cf.data,
alpha=0.1,
methodType="GD",
maxIter=10,
seed=1
)
#predicting
GD10.prediction <- gradDescent.predict(GD10.model, cf.data)
#get result
GD10.prediction$predictionData
GD10.prediction$mse
GD10.prediction$rmse
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