Displaying 7 results from an estimated 7 matches for "confmtr".
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2018 Feb 26
3
Random Seed Location
...> model <- naiveBayes(`Purchase (1=yes, 0=no)` ~ ., data = InvestTechTrain)
> prob <- predict(model, newdata = InvestTechVal, type = ?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$overall[1]
> valError <- 1 ? accuracy
> confMtr
G. Classify the 18 new (out-of-sample) readers using the following
code.
> prob <- predict(model, newdata = outOfSa...
2018 Feb 27
0
Random Seed Location
...e (1=yes, 0=no)` ~ ., data = InvestTechTrain)
> > prob <- predict(model, newdata = InvestTechVal, type = ?raw?)
> > pred <- ifelse(prob[, 2] >= 0.3, 1, 0)
>
> F. Use the confusionMatrix function in the caret package to output the
> confusion matrix.
>
> > confMtr <- confusionMatrix(pred,unlist(InvestTechVal[, 3]),mode =
> ?everything?, positive = ?1?)
> > accuracy <- confMtr$overall[1]
> > valError <- 1 ? accuracy
> > confMtr
>
> G. Classify the 18 new (out-of-sample) readers using the following
> code.
> >...
2018 Mar 04
3
Random Seed Location
...es, 0=no)` ~ ., data =
InvestTechTrain)
>> prob <- predict(model, newdata = InvestTechVal, type = ?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$overall[1]
>> valError <- 1 ? accuracy
>> confMtr
>
> G. Classify the 18 new (out-of-sample) readers using the following
> code.
>&...
2018 Mar 04
0
Random Seed Location
...estTechTrain)
> >> prob <- predict(model, newdata = InvestTechVal, type = ?raw?)
> >> pred <- ifelse(prob[, 2] >= 0.3, 1, 0)
> >
> > F. Use the confusionMatrix function in the caret package to output the
> > confusion matrix.
> >
> >> confMtr <- confusionMatrix(pred,unlist(InvestTechVal[, 3]),mode =
> > ?everything?, positive = ?1?)
> >> accuracy <- confMtr$overall[1]
> >> valError <- 1 ? accuracy
> >> confMtr
> >
> > G. Classify the 18 new (out-of-sample) readers using the foll...
2018 Mar 04
2
Random Seed Location
...gt; prob <- predict(model, newdata = InvestTechVal, type = ?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$overall[1]
>> >> valError <- 1 ? accuracy
>> >> confMtr
>> >
>> > G. Classify the 18 new (out-of-sample...
2018 Mar 04
0
Random Seed Location
...dict(model, newdata = InvestTechVal, type = ?raw?)
>>>>> pred <- ifelse(prob[, 2] >= 0.3, 1, 0)
>>>>
>>>> F. Use the confusionMatrix function in the caret package to output the
>>>> confusion matrix.
>>>>
>>>>> confMtr <- confusionMatrix(pred,unlist(InvestTechVal[, 3]),mode =
>>>> ?everything?, positive = ?1?)
>>>>> accuracy <- confMtr$overall[1]
>>>>> valError <- 1 ? accuracy
>>>>> confMtr
>>>>
>>>> G. Classify the 18...
2018 Mar 05
1
Random Seed Location
...;>> 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
>>>>>> conf...