What is the accuracy value for the following task?
If there is 8 actual co-occurrence pair of the class label
and the model predicted 6 co-occurrence pair of the class label
Related
i have trained SVM classification models based on probability prediction for recognision numbers 0-9.
I have visualization of probality for every model, looks like this for number 0 -data of probability are in variable prediction0
Then i have trained final classificator and i have 1423 correct observations (from 1499) - i have vector c= containing numbers correctly predicted
What i need to do, is when was 0 correctly predicted in vector c, mark that point on red on this graf. If it helps i have "ck" containing probalities for all number prediction for every test sample, where i get maximum probality, which was my final prediction.
You can do this by using the col argument. I'll use the mtcars dataset as an example
plot(
mpg~disp,
data=mtcars,
col=ifelse(mtcars$am==0,"red","blue")
)
I am using multinom function from nnet package for multinomial logistic regression. My dataset has 3 features and 14 different classes with total of 1000 observations.
Classes that I have is: 0,1,2,3,4,5,6,7,8,9,10,11,13,15
I divide data set into proper training and calibration, where calibration has only only class of labels(say 4). Training set has all classes except 4.
Now, I train the model as
modelfit <- multinom(label ~ x1+x2+x3, data = train)
Now, I use calibration data to find predicted probabilities as:
predProb = predict(modelfit, newdata=calib_set, type="prob")
where calib_set has only three features and no column of Y.
Then, the predProb gives me the probabilities of all 16 classes except class 11 for all observations in calibration data.
Also, when I use any test data point, I get predicted probabilties of all classes except class 11.
Can, someone explain why is it missing that and how can I get predicted probabilities of all classes?
The below picture shows predicted probabiltiies for calibration data, it is missing class 11, (it can miss class 12 and 14 because that are not in the classes)
Any suggestions or advices are much appreciated.
I have a continuous variable which I divide in 3 class. Now I have run the multinomial logistics model to predict the occurrence of any particular class.
My question is can I convert the predicted probabilities of multinomial logistics model to calculated a continuous variables. If yes, what is the formula of the same.
In the binomial distribution the expected values is calculated as np. Can I use the same logic here too?
For example: I have a score which range from 0-2 in my historical series. I classify it in 3 classess (0-06, 0.6-1 and >1) and run the multinomial logistics model. If I will use the final model to score my testing data. I would get the output as a probability. Is there any way I can convert this probability into a numeric values which can represent my score?
Given an input matrix and a correlation Rho, I want to generate a random matrix that is correlated to the input matrix with a correlation value of Rho.
I can create random matrices through rnorm, but I'm not sure how to force this new matrix to be correlated to the original input matrix.
I looked through some other posts such as this but couldn't find what I was looking for. For example, this post Generating random correlation matrix with given average correlation looks to calculate a random matrix, but correlated to itself, not an input matrix.
I have used caret library to compute class probabilities and predictions for a binary classification problem, using 10-fold cross validation and 5 times repetition.
Now I have TRUE (observed values for each data point) values, PREDICTED (by an algorithm) values, Class 0 probabilities and Class 1 probabilities which were used by an algorithm to predict class label.
Now how can I create an roc object using either ROCR or pROC library and then calculate auc value?
Assume that I have all these values stored in predictions dataframe. e.g. predictions$pred and predictions$obs are the predicted and true values respectively, and so on...
Since you did not provide a reproducible example, I'm assuming you have a binary classification problem and you predict on Class that are either Good or Bad.
predictions <- predict(object=model, test[,predictors], type='prob')
You can do:
> pROC::roc(ifelse(test[,"Class"] == "Good", 1, 0), predictions[[2]])$auc
# Area under the curve: 0.8905