R Plot estimated conditional probability
I am currently following the Harvard EDX machine learning course but don't get how you plot the estimated conditional probability like shown below
Right now I have the following code but haven't been able to figure out how the plot is created. It is a 2-outcome classification prediction, where the dark line seperates both outcomes. This is the code I have thus far for the data
library(dslabs)
data('mnist_27')
train_rpart <- train(y ~ .,
data=mnist_27$train,
method="rpart",
tuneGrid = data.frame(cp=seq(0,0.1, length=25)))
confusionMatrix(predict(train_rpart, mnist_27$test), mnist_27$test$y)
r
edited Nov 22 at 15:48
NelsonGon
660115
asked Nov 22 at 15:27
Sven van Holten
217
add a comment |
I am currently following the Harvard EDX machine learning course but don't get how you plot the estimated conditional probability like shown below
Right now I have the following code but haven't been able to figure out how the plot is created. It is a 2-outcome classification prediction, where the dark line seperates both outcomes. This is the code I have thus far for the data
library(dslabs)
data('mnist_27')
train_rpart <- train(y ~ .,
data=mnist_27$train,
method="rpart",
tuneGrid = data.frame(cp=seq(0,0.1, length=25)))
confusionMatrix(predict(train_rpart, mnist_27$test), mnist_27$test$y)
r
edited Nov 22 at 15:48
NelsonGon
660115
asked Nov 22 at 15:27
Sven van Holten
217
The code has nothing to do with the plot. What have you tried to produce a sample plot?
– NelsonGon
Nov 22 at 15:35
To be honest I have tried a lot but don't seem to even approach the right solution. I have also searched off the internet with no result...
– Sven van Holten
Nov 22 at 15:46
There's probably a function somewhere, defined just for that plot. But if you want to get this by hand, you could start by usingexpand.grid(x_1 = seq(0,0.6,by = 0.01), x_2 = seq(0,0.6,by = 0.01))and predicting the model outputs. Thenggplot() + geom_point(aes(x = x_1,y = x_2, colour = predicted_value))will get you a somewhat similar plot
– Nutle
Nov 22 at 21:38
add a comment |
I am currently following the Harvard EDX machine learning course but don't get how you plot the estimated conditional probability like shown below
Right now I have the following code but haven't been able to figure out how the plot is created. It is a 2-outcome classification prediction, where the dark line seperates both outcomes. This is the code I have thus far for the data
library(dslabs)
data('mnist_27')
train_rpart <- train(y ~ .,
data=mnist_27$train,
method="rpart",
tuneGrid = data.frame(cp=seq(0,0.1, length=25)))
confusionMatrix(predict(train_rpart, mnist_27$test), mnist_27$test$y)
r
edited Nov 22 at 15:48
NelsonGon
660115
asked Nov 22 at 15:27
Sven van Holten
217
I am currently following the Harvard EDX machine learning course but don't get how you plot the estimated conditional probability like shown below
Right now I have the following code but haven't been able to figure out how the plot is created. It is a 2-outcome classification prediction, where the dark line seperates both outcomes. This is the code I have thus far for the data
library(dslabs)
data('mnist_27')
train_rpart <- train(y ~ .,
data=mnist_27$train,
method="rpart",
tuneGrid = data.frame(cp=seq(0,0.1, length=25)))
confusionMatrix(predict(train_rpart, mnist_27$test), mnist_27$test$y)
r
r
edited Nov 22 at 15:48
NelsonGon
660115
asked Nov 22 at 15:27
Sven van Holten
217
edited Nov 22 at 15:48
NelsonGon
660115
asked Nov 22 at 15:27
Sven van Holten
217
edited Nov 22 at 15:48
NelsonGon
660115
edited Nov 22 at 15:48
NelsonGon
660115
edited Nov 22 at 15:48
NelsonGon
660115
660115
asked Nov 22 at 15:27
Sven van Holten
217
asked Nov 22 at 15:27
Sven van Holten
217
asked Nov 22 at 15:27
Sven van Holten
217
217
The code has nothing to do with the plot. What have you tried to produce a sample plot?
– NelsonGon
Nov 22 at 15:35
To be honest I have tried a lot but don't seem to even approach the right solution. I have also searched off the internet with no result...
– Sven van Holten
Nov 22 at 15:46
There's probably a function somewhere, defined just for that plot. But if you want to get this by hand, you could start by usingexpand.grid(x_1 = seq(0,0.6,by = 0.01), x_2 = seq(0,0.6,by = 0.01))and predicting the model outputs. Thenggplot() + geom_point(aes(x = x_1,y = x_2, colour = predicted_value))will get you a somewhat similar plot
– Nutle
Nov 22 at 21:38
add a comment |
The code has nothing to do with the plot. What have you tried to produce a sample plot?
– NelsonGon
Nov 22 at 15:35
To be honest I have tried a lot but don't seem to even approach the right solution. I have also searched off the internet with no result...
– Sven van Holten
Nov 22 at 15:46
There's probably a function somewhere, defined just for that plot. But if you want to get this by hand, you could start by usingexpand.grid(x_1 = seq(0,0.6,by = 0.01), x_2 = seq(0,0.6,by = 0.01))and predicting the model outputs. Thenggplot() + geom_point(aes(x = x_1,y = x_2, colour = predicted_value))will get you a somewhat similar plot
– Nutle
Nov 22 at 21:38
The code has nothing to do with the plot. What have you tried to produce a sample plot?
– NelsonGon
Nov 22 at 15:35
The code has nothing to do with the plot. What have you tried to produce a sample plot?
– NelsonGon
Nov 22 at 15:35
To be honest I have tried a lot but don't seem to even approach the right solution. I have also searched off the internet with no result...
– Sven van Holten
Nov 22 at 15:46
To be honest I have tried a lot but don't seem to even approach the right solution. I have also searched off the internet with no result...
– Sven van Holten
Nov 22 at 15:46
There's probably a function somewhere, defined just for that plot. But if you want to get this by hand, you could start by using
expand.grid(x_1 = seq(0,0.6,by = 0.01), x_2 = seq(0,0.6,by = 0.01)) and predicting the model outputs. Then ggplot() + geom_point(aes(x = x_1,y = x_2, colour = predicted_value)) will get you a somewhat similar plot– Nutle
Nov 22 at 21:38
There's probably a function somewhere, defined just for that plot. But if you want to get this by hand, you could start by using
expand.grid(x_1 = seq(0,0.6,by = 0.01), x_2 = seq(0,0.6,by = 0.01)) and predicting the model outputs. Then ggplot() + geom_point(aes(x = x_1,y = x_2, colour = predicted_value)) will get you a somewhat similar plot– Nutle
Nov 22 at 21:38
add a comment |
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The code has nothing to do with the plot. What have you tried to produce a sample plot?
– NelsonGon
Nov 22 at 15:35
To be honest I have tried a lot but don't seem to even approach the right solution. I have also searched off the internet with no result...
– Sven van Holten
Nov 22 at 15:46
There's probably a function somewhere, defined just for that plot. But if you want to get this by hand, you could start by using
expand.grid(x_1 = seq(0,0.6,by = 0.01), x_2 = seq(0,0.6,by = 0.01))and predicting the model outputs. Thenggplot() + geom_point(aes(x = x_1,y = x_2, colour = predicted_value))will get you a somewhat similar plot– Nutle
Nov 22 at 21:38