prediction

问题 In a previous question (No zeros predicted from zeroinfl object in R?) there was a great answer explaining why the predicted count distribution from a pscl package ZINB model using the function zeroinfl included so few zeros, and how one would use the different type arguments of the predict.zeroinfl function to generate a predicted count distribution that better reflected the data. I am running into the same problem, except I am using glmmTMB instead of zeroinfl for a variety of reasons

问题 This is similar to this question: Weka Predictions to CSV, but from the command line. I have the following Weka command: java -Xmx10G weka.classifiers.meta.FilteredClassifier \ -t test_data.arff -d prediction.model -p first -no-cv \ -F "weka.filters.unsupervised.attribute.Remove -R 1" \ -W hr.irb.fastRandomForest.FastRandomForest \ -- -I 512 -K 0 -S 512 Which gives me the following data: === Predictions on training data === inst# actual predicted error prediction (primary_key) 1 1:0 1:0 0.996

问题 How to predict a new given value of body using the ml2 model below, and interpret its output (new predicted output only, not model) Using Animals dataset from MASS package to build a simple linear regression model ml2<-lm(log(brain)~log(body),data=Animals) predict a new given body of 468 pred_body<-data.frame(body=c(468)) predict(ml2,new, interval="confidence") fit lwr upr 1 5.604506 4.897498 6.311513 But i am not so sure predicted y(brain) =5.6 or log(brain)=5.6? How could we get the

问题 I'm interested in tinkering directly with the contents of the BTB (branch target buffer) and GHB on the Cortex A8. The ARM manual says stuff like: To write one entry in the instruction side GHB array, for example: LDR R0, =0x3333AAAA; MCR p15, 0, R0, c15, c1, 0; Move R0 to I-L1 Data 0 Register LDR R1, =0x0000020C; MCR p15, 0, R1, c15, c5, 2; Write I-L1 Data 0 Register to GHB To read one entry in the instruction side GHB array, for example: LDR R1, =0x0000020C; MCR p15, 0, R1, c15, c7, 2; Read

问题 I am very new to Azure Machine Learning things, one of our client use to sell some fresh products to business people. They have a 'suggested buy' system, a feature will suggest some quantities to buy based on customer's sales history. After client came to know about Microsoft's Azure ML, they want to use that prediction system to suggest quantities to customers. We have sales data with these columns, CustomerName ItemName OrderDate QuantityPurchased QuantitySold We would like customers have

问题 I am struggling to understand how, in R, to generate predictive simulations for new data using a multilevel linear regression model with a single set of random intercepts. Following the example on pp. 146-147 of this text, I can execute this task for a simple linear model with no random effects. What I can't wrap my head around is how to extend the set-up to accommodate random intercepts for a factor added to that model. I'll use iris and some fake data to show where I'm getting stuck. I'll

问题 I have a series like: df['ID'] = ['ABC123', 'IDF345', ...] I'm using scikit's LabelEncoder to convert it to numerical values to be fed into the RandomForestClassifier . During the training, I'm doing as follows: le_id = LabelEncoder() df['ID'] = le_id.fit_transform(df.ID) But, now for testing/prediction, when I pass in new data, I want to transform the 'ID' from this data based on le_id i.e., if same values are present then transform it according to the above label encoder, otherwise assign a

问题 I am planning on using LibSVM to predict user authenticity in web applications. (1) Collect Data on particular user behavior(eg. LogIn time, IP Address, Country etc.) (2) Use Collected Data to train an SVM (3) Use real time data to compare and generate an output on level of authenticity Can some one tell me how can I do such a thing with LibSVM? Can Weka be helpful in these types of problems? 回答1: The three steps you mention are an outline of the solution. In some more detail: Make sure you

问题 Whats the difference between predict(rf, newdata=testSet) and predict(rf$finalModel, newdata=testSet) i train the model with preProcess=c("center", "scale") tc <- trainControl("repeatedcv", number=10, repeats=10, classProbs=TRUE, savePred=T) rf <- train(y~., data=trainingSet, method="rf", trControl=tc, preProc=c("center", "scale")) and i receive 0 true positives when i run it on a centered and scaled testSet testSetCS <- testSet xTrans <- preProcess(testSetCS) testSetCS<- predict(xTrans,

问题 I am trying to predict the hygrothermal response of a wall, given the interior and exterior climate. Based on literature research, I believe this should be possible with RNN but I have not been able to get good accuracy. The dataset has 12 input features (time-series of exterior and interior climate data) and 10 output features (time-series of hygrothermal response), both containing hourly values for 10 years. This data was created with hygrothermal simulation software, there is no missing