# Tag Info

## New answers tagged r

1

The roc function in the pROC package allows you to extract the sensitivity and specificity values. I will give an example below. Keep in mind that the $y$-axis is sensitivity, but the $x$-axis is $1 - specificity$. library(pROC) set.seed(2021) N <- 1000 x1 <- rnorm(N) x2 <- rnorm(N) x3 <- rnorm(N) z <- x1 + x2 + x3 pr <- 1/(1 + exp(-z)) y &...

1

You can use the general train from caret to train the model The new entry needs to be added in the form of the Train set, only then it will be able to predict I would have done this like this: library(caret) model_knn<-train(Species ~ ., data = db_class[row_train,], method = "knn",tuneLength = 10) #You can select any other tune length too. ...

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Your output is showing the death for Kedah only, but it is printing Johor in the title. Instead of editing it every time in the ggplot2 code, I prefer to create a separate list and filter it out in the ggplot2 code. And, instead of glue, I used a simple paste0. Solution: selected_state <- 'Kedah' death_state%>% filter(State %in% selected_state)%>%...

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Here is a solution by using bisect Python standard library from bisect import bisect from random import sample data = sample(range(10_000), 1_000) breakpoints = [1, 5, 25, 50, 150, 250, 1_000, 5_000, 10_000] buckets = {} for i in data: buckets.setdefault(breakpoints[bisect(breakpoints, i)], []).append(i) this will result in a dictionary with ...

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The question is why was scikit designed this way. Only a few people can factually answer that question. I have my opinion, but that is all that it is. However formulas can be used with scikit or statsmodels or other packages. Patsy gives the ability. This can be used with scikit as the output of Patsy functions a lot like numpy arrays. An example is here. ...

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To determine whether a time series is additive or multiplicative we can use seasonal_decompose which provides us 3 seperate components trend,seasonility,and residual.We can check the variance of seasonality and residual components for additive and multiplicative decompose. The seasonality and residual components with constant variance represent the time ...

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