calibration

In ETAS INCA, is there a way to quickly retrieve an item from an arbitrary path in an ASAP2Project?

折月煮酒 提交于 2021-02-07 11:12:53
问题 I'm trying to connect to INCA through the .NET API in order to navigate the folder structure of an ASAP2 project. Specifically, I want to get an object that represents the "Target" folder that I've highlighted below. This is proving pretty tricky--The API provides a ton of classes that have the name "Folder" in them: Folder , Asap2ProjectFolder , and IncaFolder . So what do I need to know in order to retrieve the "Target" folder? 回答1: Your first impulse might be to think of the Target folder

Problems with using plotCalibration() from the predictABEL package in R

烈酒焚心 提交于 2021-01-28 06:33:15
问题 I’ve been having some trouble with the plotCalibration() function, I have managed to get it to work before, but recently whilst working with another dataset (here is a link to the .Rda data file), I have been unable to shake off an error message which keeps cropping up: > plotCalibration(data = data, cOutcome = 2, predRisk = data$sortmort) Error in plotCalibration(data = data, cOutcome = 2, predRisk = data$sortmort) : The specified outcome is not a binary variable.` When I’ve tried to set the

Multiclass classification: probabilities and calibration

橙三吉。 提交于 2020-02-21 07:02:49
问题 I'm working on a multiclass classification problem with different classifiers, working with Python and scikit-learn. I want to use the predicted probabilities, basically to compare the predicted probabilities of the different classifiers for a specific case. I started reading about 'calibration' (here and here for example) and I became confused. For what I understood: a well-calibrated probability means that that a probability also reflects the fraction of a certain class. 1) Does this imply

Multiclass classification: probabilities and calibration

有些话、适合烂在心里 提交于 2020-02-21 07:02:04
问题 I'm working on a multiclass classification problem with different classifiers, working with Python and scikit-learn. I want to use the predicted probabilities, basically to compare the predicted probabilities of the different classifiers for a specific case. I started reading about 'calibration' (here and here for example) and I became confused. For what I understood: a well-calibrated probability means that that a probability also reflects the fraction of a certain class. 1) Does this imply

Calibration (inverse prediction) from LOESS object in R

限于喜欢 提交于 2020-01-22 03:32:18
问题 I have fit a LOESS local regression to some data and I want to be able to find the X value associated with a given Y value. plot(cars, main = "Stopping Distance versus Speed") car_loess <- loess(cars$dist~cars$speed,span=.5) lines(1:50, predict(car_loess,data.frame(speed=1:50))) I was hoping that I could use teh inverse.predict function from the chemCal package, but that does not work for LOESS objects. Does anyone have any idea how I might be able to do this calibrationa in a better way than

finding speed of device movement

杀马特。学长 韩版系。学妹 提交于 2020-01-07 02:44:08
问题 I need to find velocity of a device(Samsung Galaxy s3). I've now read quite a lot of stackoverflow questions concerning it, but still confused as for what I should use. My observations so far: 1) Somebody did this to find velocity from accelerometer sensor's data. But in my case the device's movement will not have constant slope or straight trajectory. 2) Integration will give much drift. 3) Kalman filtering can be used but it's hard to implement and CPU consuming 4) Complementary filter can