Displaying 7 results from an estimated 7 matches for "ntrain".
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2007 May 10
3
how to control the sampling to make each sample unique
I have a dataset of 10000 records which I want to use to compare two
prediction models.
I split the records into test dataset (size = ntest) and training dataset
(size = ntrain). Then I run the two models.
Now I want to shuffle the data and rerun the models. I want many shuffles.
I know that the following command
sample ((1:10000), ntrain)
can pick ntrain numbers from 1 to 10000. Then I just use these rows as the
training dataset.
But how can I make sure each run of...
2008 Oct 15
1
Forecasting using ARIMAX
...1998,245.490
Feb 1998,670,Feb 1998,421.25,Feb 1998,288.170
Mar 1998,642.5,Mar 1998,395,Mar 1998,254.950
Apr 1998,610,Apr 1998,377.5,Apr 1998,230.640
:
> (nrowDepVar <- nrow(depVar))
[1] 545
> (nTest <- nInstance + nHorizon - 1) #number of latest points reserved for testing
[1] 13
> (nTrain <- nrowDepVar - nTest)
[1] 532
First I use auot.arima to find the best (p,d,q).
> modArima <- auto.arima(depVar[1:nTrain,], trace=TRUE)
ARIMA(2,1,2) with drift : 4402.637
ARIMA(0,1,0) with drift : 4523.553
ARIMA(1,1,0) with drift : 4410.036
ARIMA(0,1,1) with...
2005 Jan 18
1
Interpretation of randomForest results
...e training set?
The results you showed above are out-of-bag (OOB) results. If you don't
know what that means, you should read the documentation, and perhaps the
references.
> But when I run below command to test the performance of
> classification in the same training set.
>
> ntrain <- read.table("train10.dat", header = T)
> ntrain.pred <- predict(oz.rf, ntrain)
> table(observed = ntrain[, "LESION"], predicted = ntrain.pred)
>
> I got the following results. It seemed that the
> classification rates for 'lesion' and 'noninf...
2014 Jul 02
0
How do I call a C++ function (for k-means) within R?
...interaction.depth=as.integer(interaction.depth),
n.minobsinnode=as.integer(n.minobsinnode),
n.classes = as.integer(nClass),
shrinkage=as.double(shrinkage),
bag.fraction=as.double(bag.fraction),
nTrain=as.integer(nTrain),
fit.old=as.double(NA),
n.cat.splits.old=as.integer(0),
n.trees.old=as.integer(0),
verbose=as.integer(verbose),
PACKAGE = "gbm")
names(gbm.obj) <- c("initF&qu...
2013 Mar 24
3
Parallelizing GBM
...it for speed reasons, and I usually call it this
way
gbm_model <- gbm.fit(trainRF,prices_train,
offset = NULL,
misc = NULL,
distribution = "multinomial",
w = NULL,
var.monotone = NULL,
n.trees = 50,
interaction.depth = 5,
n.minobsinnode = 10,
shrinkage = 0.001,
bag.fraction = 0.5,
nTrain = (n_train/2),
keep.data = FALSE,
verbose = TRUE,
var.names = NULL,
response.name = NULL)
Does anybody know an easy way to parallelize the model (in this case it
means simply having 4 cores on the same machine working on the problem)?
Any suggestion is welcome.
Cheers
Lorenzo
2013 Jun 23
1
Which is the final model for a Boosted Regression Trees (GBM)?
...[5] "oobag.improve" "trees"
[7] "c.splits" "bag.fraction"
[9] "distribution" "interaction.depth"
[11] "n.minobsinnode" "n.trees"
[13] "nTrain" "response.name"
[15] "shrinkage" "train.fraction"
[17] "var.levels" "var.monotone"
[19] "var.names" "var.type"
[21] "verbose"...
2008 Sep 16
0
Warning messages after auto.arima
...ng thru the value at the
console screen.
For those models with drift, how can I find out what are the drifts? For
example, eyeballing thru, my 2nd best model is ARIMA(1,1,1) with drift but
it didn't state what is the drift.
Many thanks.
> modelarima <- auto.arima(Price[1:nTrain], trace=TRUE)
ARIMA(2,1,2) with drift : 4417.541
ARIMA(0,1,0) with drift : 4538.817
ARIMA(1,1,0) with drift : 4424.534
ARIMA(0,1,1) with drift : 4457.507
ARIMA(1,1,2) with drift : 4416.070
ARIMA(1,1,1) with drift : 4414.328...