random-forest

问题 Background: I can make a random Forest in R: set.seed(1) library(randomForest) data(iris) model.rf <- randomForest(Species ~ ., data=iris, importance=TRUE, ntree=20, mtry = 2) I can predict values using the randomForest object that I just made: my_pred <- predict(model.rf) plot(iris$Species,my_pred) I can then peel off some random tree from the forest: idx <- sample(x = 1:20,size = 1,replace = F) single_tree <- getTree(model.rf,k=1) Questions: How do I predict from a single tree pulled from

问题 I am using the RandomForestClassifier from pyspark.ml.classification I run the model on a binary class dataset and display the probabilities. I have the following in the col probabilities : +-----+----------+---------------------------------------+ |label|prediction|probability | +-----+----------+---------------------------------------+ |0.0 |0.0 |[0.9005918461098429,0.0994081538901571]| |1.0 |1.0 |[0.6051335859900139,0.3948664140099861]| +-----+----------+-----------------------------------

问题 I am struggling to pull out the feature importances from my RandomForestRegressor, I get an: AttributeError: 'GridSearchCV' object has no attribute 'feature_importances_'. Anyone know why there is no attribute? According to documentation there should exist this attribute? The full code: from sklearn.ensemble import RandomForestRegressor from sklearn.model_selection import GridSearchCV #Running a RandomForestRegressor GridSearchCV to tune the model. parameter_candidates = { 'n_estimators' :

问题 So I am attempting to run the 'genie3' algorithm (ref: http://homepages.inf.ed.ac.uk/vhuynht/software.html) in R which uses the 'randomForest' method. I am running into the following Error: > weight.matrix<-get.weight.matrix(tmpLog2FC, input.idx=1:4551) Starting RF computations with 1000 trees/target gene, and 67 candidate input genes/tree node Computing gene 1/11805 Show Traceback Rerun with Debug Error in randomForest.default(x, y, mtry = mtry, ntree = nb.trees, importance = TRUE, : NA not

问题 I am new to caret, and I just want to ensure that I fully understand what it’s doing. Towards that end, I’ve been attempting to replicate the results I get from a randomForest() model using caret’s train() function for method="rf". Unfortunately, I haven’t been able to get matching results, and I’m wondering what I’m overlooking. I’ll also add that given that randomForest uses bootstrapping to generate samples to fit each of the ntrees, and estimates error based on out-of-bag predictions, I’m

问题 I'm using spark.mllib.classification.{LogisticRegressionModel, LogisticRegressionWithSGD} and spark.mllib.tree.RandomForest for classification. Using these packages I produce classification models. Only these models predict a specific class per instance. In Weka, we can get the exact probability for each instance to be of each class. How can we do it using these packages? In LogisticRegressionModel we can set the threshold. So I've created a function that check the results for each point on a

问题 I'm using spark.mllib.classification.{LogisticRegressionModel, LogisticRegressionWithSGD} and spark.mllib.tree.RandomForest for classification. Using these packages I produce classification models. Only these models predict a specific class per instance. In Weka, we can get the exact probability for each instance to be of each class. How can we do it using these packages? In LogisticRegressionModel we can set the threshold. So I've created a function that check the results for each point on a

问题 Im trying to write a for loop to create various random forest models. I've stored the variables I would like to use in the different models in a list called list: list <- c("EXPG1 + EXPG2", "EXPG1 + EXPG2 + distance") Then I try to loop over it created predictions. What I finally want to achieve is this: modFit1 <- train(won ~ EXPG1 + EXPG2, data=training, method="rf", prox=TRUE) modFit2 <- train(won ~ EXPG1 + EXPG2 + distance, data=training, method="rf", prox=TRUE) I have some issues trying

问题 Im trying to write a for loop to create various random forest models. I've stored the variables I would like to use in the different models in a list called list: list <- c("EXPG1 + EXPG2", "EXPG1 + EXPG2 + distance") Then I try to loop over it created predictions. What I finally want to achieve is this: modFit1 <- train(won ~ EXPG1 + EXPG2, data=training, method="rf", prox=TRUE) modFit2 <- train(won ~ EXPG1 + EXPG2 + distance, data=training, method="rf", prox=TRUE) I have some issues trying

问题 Im trying to write a for loop to create various random forest models. I've stored the variables I would like to use in the different models in a list called list: list <- c("EXPG1 + EXPG2", "EXPG1 + EXPG2 + distance") Then I try to loop over it created predictions. What I finally want to achieve is this: modFit1 <- train(won ~ EXPG1 + EXPG2, data=training, method="rf", prox=TRUE) modFit2 <- train(won ~ EXPG1 + EXPG2 + distance, data=training, method="rf", prox=TRUE) I have some issues trying