random-forest

问题 Closed . This question needs details or clarity. It is not currently accepting answers. Want to improve this question? Add details and clarify the problem by editing this post. Closed 5 years ago . I was wondering whether and how it is possible to use a python generator as data input to scikit-learn classifier's .fit() functions? Due to huge amounts of data, this seems to make sense to me. In particular I am about to implement a random forest approach. Regards K 回答1: The answer is "no". To do

问题 I recently built a random forest model using the ranger package in R. However, I noticed that the predictions stored in the ranger object during training (accessible with model$predictions) do not match the prediction I get if I run the predict command on the same dataset using the model created. The following code reproduces the problem on the mtcars dataset. I created a binary variable just for the sake of converting this to a classification problem though I saw similar results with

问题 What am I doing? I am solving a classification problem using Random Forests. I have a set of strings of a fixed length (10 characters long) that represent DNA sequences. DNA alphabet consists of 4 letters, namely A , C , G , T . Here's a sample of my raw data: ATGCTACTGA ACGTACTGAT AGCTATTGTA CGTGACTAGT TGACTATGAT Each DNA sequence comes with experimental data describing a real biological response; the molecule was seen to elicit biological response (1), or not (0). Problem: The training set

问题 I fitted a random forest for my multinomial target with the randomForest package in R. Looking for the variable importance I found out permutation accuracy importance which is what I was looking for my analysis. I fitted a random forest with the h2o package too, but the only measures it shows me are relative_importance, scaled_importance, percentage . My question is: can I extract a measure that shows me the level of the target which better classify the variable i want to take in exam?

问题 I am trying to predict a raster layer with randomForest and the caret package, but fail when I introduce factor variables. Without factors, everything works fine, but as soon as I bring a factor in, I get the error: Error in predict.randomForest(modelFit, newdata) : Type of predictors in new data do not match that of the training data. I have created some sample code below that walks through he process. I present it in a few steps for transparency and to provide a working example. (To skip

问题 First some background info, which is probably more interesting on stats.stackexchange: In my data analysis I try to compare the performance of different machine learning methods on time series data (regression, not classification). So for example I have trained a Boosting trained model and compare this with a Random Forest trained model (R package randomForest). I use time series data where the explanatory variables are lagged values of other data and the dependent variable. For some reason

问题 I have been trying to install bigmemory on my R installation. My OS is windows 7 64 bit and I have tried it on R V2.15.1,2.15.2 and 3.0.1 64 bit but I cant get it to work. I have tried several options download the current source and run the command in R v3.0.1 install.packages("D:/Downloads/bigmemory_4.4.3.tar.gz", repos = NULL, type="source") but this gives an error "ERROR: Unix-only package" download older sources and run a similar commands, in the various installations of R V2 V3 etc, This

问题 Related to these: getting this error in Caret https://github.com/topepo/caret/issues/160 I'm getting this error: Something is wrong; all the Accuracy metric values are missing: Accuracy Kappa Min. : NA Min. : NA 1st Qu.: NA 1st Qu.: NA Median : NA Median : NA Mean :NaN Mean :NaN 3rd Qu.: NA 3rd Qu.: NA Max. : NA Max. : NA NA's :5 NA's :5 Error in train.default(x, y, weights = w, ...) : Stopping In addition: Warning message: In nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo

问题 I am using randomForest function from randomForest package to find the most important variable: my dataframe is called urban and my response variable is revenue which is numeric. urban.random.forest <- randomForest(revenue ~ .,y=urban$revenue, data = urban, ntree=500, keep.forest=FALSE,importance=TRUE,na.action = na.omit) I get the following error: Error in randomForest.default(m, y, ...) : data (x) has 0 rows on the source code it is related to x variable: n <- nrow(x) p <- ncol(x) if (n ==

问题 I originally had a data frame composed of 12 columns in N rows. The last column is my class (0 or 1). I had to convert my entire data frame to numeric with training <- sapply(training.temp,as.numeric) But then I thought I needed the class column to be a factor column to use the randomforest() tool as a classifier, so I did training[,"Class"] <- factor(training[,ncol(training)]) I proceed to creating the tree with training_rf <- randomForest(Class ~., data = trainData, importance = TRUE, do