*My code is below:*
library('RMySQL')
library('DMwR')
library('tm')
library('Snowball')
library('SnowballC')
con <- dbConnect(MySQL(), user="root", password="stuff0645",
dbname="TwitterCelebs", host="localhost")
rt_outlier <- dbGetQuery(con, "SELECT *,tweet_text from
outlier_info,tweets where ou
What you are asking is a bad idea on multiple levels. You will grossly
over-estimate the area under the ROC curve. Consider the 1-NN model: you
will have perfect predictions every time.
To do this, you will need to run train again and modify the index and
indexOut objects:
library(caret)
set.s
Hello,
I am using caret package in order to train a K-Nearest Neigbors algorithm. For
this, I am running this code:
Control <- trainControl(method="cv", summaryFunction=twoClassSummary,
classProb=T)
tGrid=data.frame(k=1:100)
trainingInfo <- train(Formula, data=trainData, method = "knn",tuneGr
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