问题
This seems most related to: How to get the probability per instance in classifications models in spark.mllib
I'm doing a classification task with spark ml, building a MultilayerPerceptronClassifier. Once I build a model, I can get a predicted class given an input vector, but I can't get the probability for each output class. The above listing indicates that NaiveBayesModel supports this functionality as of Spark 1.5.0 (using a predictProbabilities method). I would like to get at this functionality for the MLPC. Is there a way I can hack at it to get my probabilities? Will it be included in 1.6.2?
回答1:
If you take a look at this line in the MLPC source code, you can see that the MLPC is working from an underlying TopologyModel which provides the .predict method I'm looking for. The MLPC decodes the resulting Vector into a single label.
I'm able to use the trained MLPC model to create a new TopologyModel using its weights:
MultilayerPerceptronClassifier trainer = new MultilayerPerceptronClassifier()...;
MultilayerPerceptronClassificationModel model = trainer.fit(trainingData);
TopologyModel topoModel = FeedForwardTopology.multiLayerPerceptron(model.layers(), true).getInstance(model.weights());
回答2:
I think the short answer is No.
The MultilayerPerceptronClassifier is not probabilistic. When the weights (and any biases) are set after training, the classification for a given input will always be the same.
What you're really asking, I think, is "if I were to tweak the weights by certain random disturbances of a given magnitude, how likely would the classification be the same as without the tweaks?"
You could do an ad hoc probability calculation by re-training the perceptron (with different, randomly chosen starting conditions) and get some idea of the probability of various classifications.
But I don't think this is really part of the expected behavior of a MLPC.
来源：https://stackoverflow.com/questions/35962952/how-to-get-classification-probabilities-from-multilayerperceptronclassifier