Displaying 7 results from an estimated 7 matches for "investtechtrain".
2018 Feb 26
3
Random Seed Location
...e[, 1] <- cut_number(outOfSample[, 1], n = 20)
> outOfSample[, 2] <- cut_number(outOfSample[, 2], n = 20)
D. Partition the original (in-sample) data into 60% training and 40%
validation sets.
> n <- nrow(InvestTech)
> train <- sample(1:n, size = 0.6 * n, replace = FALSE)
> InvestTechTrain <- InvestTech[train, ]
> InvestTechVal <- InvestTech[-train, ]
E. Use the naiveBayes function in the e1071 package to fit the model.
> model <- naiveBayes(`Purchase (1=yes, 0=no)` ~ ., data = InvestTechTrain)
> prob <- predict(model, newdata = InvestTechVal, type = ?raw?)
>...
2018 Feb 27
0
Random Seed Location
...= 20)
> > outOfSample[, 2] <- cut_number(outOfSample[, 2], n = 20)
>
> D. Partition the original (in-sample) data into 60% training and 40%
> validation sets.
>
> > n <- nrow(InvestTech)
> > train <- sample(1:n, size = 0.6 * n, replace = FALSE)
> > InvestTechTrain <- InvestTech[train, ]
> > InvestTechVal <- InvestTech[-train, ]
>
> E. Use the naiveBayes function in the e1071 package to fit the model.
>
> > model <- naiveBayes(`Purchase (1=yes, 0=no)` ~ ., data = InvestTechTrain)
> > prob <- predict(model, newdata =...
2018 Mar 04
3
Random Seed Location
...0)
>> outOfSample[, 2] <- cut_number(outOfSample[, 2], n = 20)
>
> D. Partition the original (in-sample) data into 60% training and 40%
> validation sets.
>
>> n <- nrow(InvestTech)
>> train <- sample(1:n, size = 0.6 * n, replace = FALSE)
>> InvestTechTrain <- InvestTech[train, ]
>> InvestTechVal <- InvestTech[-train, ]
>
> E. Use the naiveBayes function in the e1071 package to fit the
model.
>
>> model <- naiveBayes(`Purchase (1=yes, 0=no)` ~ ., data =
InvestTechTrain)
>> prob <- predict(model, newd...
2018 Mar 04
0
Random Seed Location
...] <- cut_number(outOfSample[, 2], n = 20)
> >
> > D. Partition the original (in-sample) data into 60% training and 40%
> > validation sets.
> >
> >> n <- nrow(InvestTech)
> >> train <- sample(1:n, size = 0.6 * n, replace = FALSE)
> >> InvestTechTrain <- InvestTech[train, ]
> >> InvestTechVal <- InvestTech[-train, ]
> >
> > E. Use the naiveBayes function in the e1071 package to fit the model.
> >
> >> model <- naiveBayes(`Purchase (1=yes, 0=no)` ~ ., data = InvestTechTrain)
> >> prob <...
2018 Mar 04
2
Random Seed Location
...le[, 2], n = 20)
>> >
>> > D. Partition the original (in-sample) data into 60% training and 40%
>> > validation sets.
>> >
>> >> n <- nrow(InvestTech)
>> >> train <- sample(1:n, size = 0.6 * n, replace = FALSE)
>> >> InvestTechTrain <- InvestTech[train, ]
>> >> InvestTechVal <- InvestTech[-train, ]
>> >
>> > E. Use the naiveBayes function in the e1071 package to fit the model.
>> >
>> >> model <- naiveBayes(`Purchase (1=yes, 0=no)` ~ ., data = InvestTechTrain)
&g...
2018 Mar 04
0
Random Seed Location
...>>>
>>>> D. Partition the original (in-sample) data into 60% training and 40%
>>>> validation sets.
>>>>
>>>>> n <- nrow(InvestTech)
>>>>> train <- sample(1:n, size = 0.6 * n, replace = FALSE)
>>>>> InvestTechTrain <- InvestTech[train, ]
>>>>> InvestTechVal <- InvestTech[-train, ]
>>>>
>>>> E. Use the naiveBayes function in the e1071 package to fit the model.
>>>>
>>>>> model <- naiveBayes(`Purchase (1=yes, 0=no)` ~ ., data = Inve...
2018 Mar 05
1
Random Seed Location
...tition the original (in-sample) data into 60% training and
>>>>> 40%
>>>>> validation sets.
>>>>>
>>>>>> n <- nrow(InvestTech)
>>>>>> train <- sample(1:n, size = 0.6 * n, replace = FALSE)
>>>>>> InvestTechTrain <- InvestTech[train, ]
>>>>>> InvestTechVal <- InvestTech[-train, ]
>>>>>
>>>>>
>>>>> E. Use the naiveBayes function in the e1071 package to fit the
>>>>> model.
>>>>>
>>>>>> mo...