prediction

问题 I know the AUC/ROC area (http://weka.wikispaces.com/Area+under+the+curve) in weka is based on the e Mann Whitney statistic (http://en.wikipedia.org/wiki/Mann-Whitney_U) But my doubt is, if I've got 10 labeled instances (Y or N, binary target attribute), by applying an algorithm (i.e. J48) onto the dataset, then there are 10 predicted labels on these 10 instances. Then what exactly should I use to calculate the AUC_Y, AUC_N, and AUC_Avg? Use the prediction's ranked label Y and N or the actual

问题 I am trying to do something close to a shallow bootstrapping but I am struggling with data type. Here is the script : library(languageR) data(dative) sub1<-dative[grepl("S10|S11",dative$Speaker),] mod_sub1<-glm(RealizationOfRecipient~Verb+SemanticClass+LengthOfRecipient+AnimacyOfRec+DefinOfRec+PronomOfRec+LengthOfTheme+AnimacyOfTheme+DefinOfTheme+PronomOfTheme+AccessOfRec+AccessOfTheme,family='binomial',data=sub1) comp_sub1<-dative[!grepl("S10|S11",dative$Speaker),] expected_compsub1 <- comp

问题 I'm struggling to generate random data set with predicted probability of multinomial logistic regression. Let's take an example. I'll use nnet package for multinomial logistic regression. I will also use wine data set in rattle.data package. library("nnet") library("rattle.data") data(wine) multinom.fit<-multinom(Type~Alcohol+Color,data=wine) summary(multinom.fit) Call: multinom(formula = Type ~ Alcohol + Color - 1, data = wine) Coefficients: Alcohol Color 2 0.6258035 -1.9480658 3 -0.3457799

问题 This question was migrated from Cross Validated because it can be answered on Stack Overflow. Migrated 5 years ago . I am using R and Java for displaying prediction. I have data of 5 hours. I want to predict 5th-hour data from four hours' data (memory with respect to date). By using 4 hours' data I am creating new collection and inserting the 5th hour's predicted data in a new collection. But I am getting the following error: The Exception is eval failed, request status: error code: 127 org

问题 The integrated Brier score (IBS) has been suggested in a paper by Graf et al (1999) as a good measure for prediction accuracy in survival models (see e.g. overview paper by Wiering et al., page 23). It was implemented in the package ipred as function sbrier . However, whereas the brier score definition obviously applies to Cox proportional hazard models, I cannot get sbrier to return the Brier score for a coxph model. Here is the problem set up. library(survival) library(ipred) data("DLBCL",

问题 I've been using BTYD package for predicting customer churn and number of orders in the future, but I find the included models ( Pareto/NBD , BG/NBD and BG/BB ) limited in the sense that they only take recency, frequency, age and monetary value into account. Using these values I receive accuracy of up to 80% but I'm sure this could be improved by incorporating more meaningful predictors into the model. Is it possible in this package? I couldn't find any information about it in the vignette.

问题 Out of interest, I am playing around with the Random class that comes with C#. I am trying to predict the future. As it is pseudo random, there must be some way to predict the numbers. The only way I can think of so far is brute force (Getting all possible seeds and finding the pattern of the random numbers in them) but I believe it will be too much processing power as the seed can be anything from -2,147,483,647 to 2,147,483,647. So far I have determined that: new Random() == new Random

问题 I've just recently started playing around with the random forest package in R. After growing my forest, I tried predicting the response using the same dataset (ie the training dataset) which gave me a confusion matrix different from the one that was printed with the forest object itself. I thought there might be something wrong with the newdata argument but I followed the example given in the documentation to the t and it gave the same problem. Here's an example using the Species dataset.

问题 I have a following( fig 1 ) unlabeled training set which I am trying to detect the outliers, have come up with a procedure to label the data with 0:normal data and 1:outlier and want to train it with SVM. I followed this instructions to train the SVM's model but when I am trying to predict the labels of same data I have trained the SVM it does not predict any( fig 2 )! fig 1: the support vectors after training fig 2: the prediction of SVM model on the same data it has been training with The

问题 I am caught in a problem here when I try to take the predictions from my training model. The scenario is: I train a neural network model to learn and classify pictures using Tensorflow. When I train in Gcloud, it returns a different results that when I train locally. Even using the same OS, libraries and code, it returns differents outputs. Some questions came on my mind: The data I am talking about is the pictures I am using to train. I) Considering that you are in the same machine, every