search for: investtechv

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2018 Feb 26
3
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...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, newdata = InvestTechVal, type = ?raw?) > pred <- ifelse(prob[, 2] >= 0.3, 1,...
2018 Feb 27
0
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...utOfSample[, 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?) > > pred &lt...
2018 Mar 04
3
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...Sample[, 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?) >> pre...
2018 Mar 04
0
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...gt; > 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 = ?r...
2018 Mar 04
2
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...on 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 = Inve...
2018 Mar 04
0
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...n-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,...
2018 Mar 05
1
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...>>> 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 = >&...