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I am trying to build a model in R with random forest classification. (By editing the code by Ned Horning) I first used randomForest package but then found ranger, which promises faster calculations.
At first, I used the code below to get predicted probabilities for each class after fitting the model with randomForest as:
predProbs <- as.data.frame(predict(randfor, imageBlock, type='prob'))
The type of probability here is as follows:
We have 500 trees in the model and 250 of them says the observation is class 1, hence the probability is 250/500 = 50%
In ranger, I realized that there is no type = 'prob' option.
I searched and tried some adjustments but couldn't get any progress. I need an object or so containing probabilities as mentioned above with ranger package.
Could anyone give some advice about the issue?
354
Theoretical probability uses math to predict the outcomes. Just divide the favorable outcomes by the possible outcomes. Experimental probability is based on observing a trial or experiment, counting the favorable outcomes, and dividing it by the total number of times the trial was performed.
Predicted probabilities are calibrated or scaled to reflect the observed occurence of class 1 events by an additional regressor that uses the initial probabilities to predict the target variable that is the true probabilities.
A random forest is a popular tool for estimating probabilities in machine learning classification tasks. However, the means by which this is accomplished is unprincipled: one simply counts the fraction of trees in a forest that vote for a certain class.
The predict() returns the true probability for each class based on votes by all the trees. Using randomForest(x,y,xtest=x,ytest=y) functions, passing a formula or simply randomForest(x,y). randomForest(x,y,xtest=x,ytest=y) would return the probability for each class.
You need to train a "probabilistic classifier"-type ranger object:
library("ranger")
iris.ranger = ranger(Species ~ ., data = iris, probability = TRUE)
This object computes a matrix (n_samples, n_classes) when used in the predict.ranger function:
probabilities = predict(iris.ranger, data = iris)$predictions
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