Displaying 7 results from an estimated 7 matches for "valerror".
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evalerror
2018 Feb 26
3
Random Seed Location
...e = ?raw?)
> pred <- ifelse(prob[, 2] >= 0.3, 1, 0)
F. Use the confusionMatrix function in the caret package to output the
confusion matrix.
> confMtr <- confusionMatrix(pred,unlist(InvestTechVal[, 3]),mode =
?everything?, positive = ?1?)
> accuracy <- confMtr$overall[1]
> valError <- 1 ? accuracy
> confMtr
G. Classify the 18 new (out-of-sample) readers using the following
code.
> prob <- predict(model, newdata = outOfSample, type = ?raw?)
> pred <- ifelse(prob[, 2] >= 0.3, 1, 0)
> cbind(pred, prob, outOfSample[, -3])
---
This email has been c...
2018 Feb 27
0
Random Seed Location
...>= 0.3, 1, 0)
>
> F. Use the confusionMatrix function in the caret package to output the
> confusion matrix.
>
> > confMtr <- confusionMatrix(pred,unlist(InvestTechVal[, 3]),mode =
> ?everything?, positive = ?1?)
> > accuracy <- confMtr$overall[1]
> > valError <- 1 ? accuracy
> > confMtr
>
> G. Classify the 18 new (out-of-sample) readers using the following
> code.
> > prob <- predict(model, newdata = outOfSample, type = ?raw?)
> > pred <- ifelse(prob[, 2] >= 0.3, 1, 0)
> > cbind(pred, prob, outOfSample[,...
2018 Mar 04
3
Random Seed Location
...0.3, 1, 0)
>
> F. Use the confusionMatrix function in the caret package to
output the
> confusion matrix.
>
>> confMtr <- confusionMatrix(pred,unlist(InvestTechVal[, 3]),mode =
> ?everything?, positive = ?1?)
>> accuracy <- confMtr$overall[1]
>> valError <- 1 ? accuracy
>> confMtr
>
> G. Classify the 18 new (out-of-sample) readers using the following
> code.
>> prob <- predict(model, newdata = outOfSample, type = ?raw?)
>> pred <- ifelse(prob[, 2] >= 0.3, 1, 0)
>> cbind(pred, prob, outOfSampl...
2018 Mar 04
0
Random Seed Location
...F. Use the confusionMatrix function in the caret package to output the
> > confusion matrix.
> >
> >> confMtr <- confusionMatrix(pred,unlist(InvestTechVal[, 3]),mode =
> > ?everything?, positive = ?1?)
> >> accuracy <- confMtr$overall[1]
> >> valError <- 1 ? accuracy
> >> confMtr
> >
> > G. Classify the 18 new (out-of-sample) readers using the following
> > code.
> >> prob <- predict(model, newdata = outOfSample, type = ?raw?)
> >> pred <- ifelse(prob[, 2] >= 0.3, 1, 0)
> >>...
2018 Mar 04
2
Random Seed Location
...onMatrix function in the caret package to output the
>> > confusion matrix.
>> >
>> >> confMtr <- confusionMatrix(pred,unlist(InvestTechVal[, 3]),mode =
>> > ?everything?, positive = ?1?)
>> >> accuracy <- confMtr$overall[1]
>> >> valError <- 1 ? accuracy
>> >> confMtr
>> >
>> > G. Classify the 18 new (out-of-sample) readers using the following
>> > code.
>> >> prob <- predict(model, newdata = outOfSample, type = ?raw?)
>> >> pred <- ifelse(prob[, 2] >= 0...
2018 Mar 04
0
Random Seed Location
...in the caret package to output the
>>>> confusion matrix.
>>>>
>>>>> confMtr <- confusionMatrix(pred,unlist(InvestTechVal[, 3]),mode =
>>>> ?everything?, positive = ?1?)
>>>>> accuracy <- confMtr$overall[1]
>>>>> valError <- 1 ? accuracy
>>>>> confMtr
>>>>
>>>> G. Classify the 18 new (out-of-sample) readers using the following
>>>> code.
>>>>> prob <- predict(model, newdata = outOfSample, type = ?raw?)
>>>>> pred <- ifelse...
2018 Mar 05
1
Random Seed Location
...ix.
>>>>>
>>>>>> confMtr <- confusionMatrix(pred,unlist(InvestTechVal[, 3]),mode =
>>>>>
>>>>> ?everything?, positive = ?1?)
>>>>>>
>>>>>> accuracy <- confMtr$overall[1]
>>>>>> valError <- 1 ? accuracy
>>>>>> confMtr
>>>>>
>>>>>
>>>>> G. Classify the 18 new (out-of-sample) readers using the following
>>>>> code.
>>>>>>
>>>>>> prob <- predict(model, newdata =...