How are gini, entropy, and log_loss calculated for the multilabel case? I'm trying to understand if multiclass classification can be seen as a special case of multilabel classification for decision trees.
Just for illustration, in MLP-Classifiers the multiclass problem gets translated into a multilabel problem internally.
I was unable to establish the calculation of the split criteria for DecisionTreeClassifiers for the multilabel case from the documentation.
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The H2OSupportVectorMachineEstimator in H2O seems to only support "gaussian" as the value of the kernel_type parameter. Is there a way to train a linear SVM with H2O?
As you mentioned, based on the documentation (https://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/svm.html) currently there is no way to train linear SVM on H2O. Within linear models, I think it only has GLM (https://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/glm.html).
I have trained a model for 3-class classification using sklearn.linear_model.SGDClassifier. Now I'm looking for a way for manual inference of the model. The problem here is that the model contains three pairs of [coef_, intercept_] so I don't understand how can I do a prediction in C++.
The code for training looks like in the sklearn example:
clf = make_pipeline(StandardScaler(), SGDClassifier(max_iter=1000, tol=1e-3))
clf.fit(train_features, train_labels)
I tried to calculate values coef_ * sample + intercept_ for each of the classes but didn't understand how to determine the class using that numbers.
I have to pre train a model for multi label classification. I'm pretraining with cifar10 dataset and I wonder if I have to use for the pre training
'categorical_crossentrpy' (softmax) or 'binary_crossentropy' (sigmoid), since in the first case I have a multi classification problem
You should use softmax because it gives you the probabilities for every class, no matter how many of them are there. Sigmoid, as you have written is used with binary_crossentropy and is used in binary classification (hence binary in the name). I hope it's clearer now.
I would like to know how to calculate the probability using Spark MLLIB SVM in a multi-class classification problem.
The documentation shows there is no such function available.
LibSVM uses Platt-scaling.
My questions are:
Is there a function to calculate the probability somewhere?
If not, who can help me implementing such functionality?
I would simply take the average distances from all the support vectors for each category after training and compare the distance from a new data-point to hyperplanes from all the classifiers.
I think the SVMModel.predict() gives these distances, but I am uncertain.
I am attempting 3 class classification by using SVM classifier. How do we interpret the probabililty estimates predicted by LIBSVM. Is it based on perpendicular distance of the instance from the maximal margin hyperplane?.
Kindly through some light on the interpretation of probability estimates predicted by LIBSVM classifier. Parameters C and gamma are first tuned and then probability estimates are outputted by using -b option with both training and testing.
Multiclass SVM is always decomposed into several binary classifiers (typically a set of one vs all classifiers). Any binary SVM classifier's decision function outputs a (signed) distance to the separating hyperplane. In short, an SVM maps the input domain to a one-dimensional real number (the decision value). The predicted label is determined by the sign of the decision value. The most common technique to obtain probabilistic output from SVM models is through so-called Platt scaling (paper of LIBSVM authors).
Is it based on perpendicular distance of the instance from the maximal margin hyperplane?
Yes. Any classifier that outputs such a one-dimensional real value can be post-processed to yield probabilities, by calibrating a logistic function on the decision values of the classifier. This is the exact same approach as in standard logistic regression.
SVM performs binary classification. In order to achieve multiclass classification libsvm performs what it's called one vs all. What you get when you invoke -bis the probability related to this technique that you can found explained here .