I have a set of svm classifiers that i would like to train under adaboost. Is there any library avaiable for download that implements an adaboost algorithm that can help me?
Weka is a Java library that has an implementation of Adaboost.
Related
i want to know is there any method by which the computer can decide which classification model to use ( Decision trees, logistic regression, KNN, etc. ) by just looking at the training data.
even just the math will be extremely helpful.
I am going to be writing this in python 3, so if there's any built method in scikit-learn or tensorflow for this purpose,it would be of great help.
This scikit learn tool kit solves it :
https://automl.github.io/auto-sklearn/stable/index.html
I study SVM and I will implement svm using python sklearn.svm.SVC.
As i know SVM problem can be represented a QP(Quadratic Programming)
So here i was wondering which QP solver is used to solve the SVM QP problem in sklearn svm.
I think it may be SMO or coordinate descent algorithm.
Please let me know what the exact algorithm is used in sklearn svm
Off-the-shelf QP-solvers have been used in the past, but for many years now dedicated code is used (much faster and more robust). Those solvers are not (general) QP-solvers anymore and are just build for this one use-case.
sklearn's SVC is a wrapper for libsvm (proof).
As the link says:
Since version 2.8, it implements an SMO-type algorithm proposed in this paper:
R.-E. Fan, P.-H. Chen, and C.-J. Lin. Working set selection using second order information for training SVM. Journal of Machine Learning Research 6, 1889-1918, 2005.
(link to paper)
I've been using a bunch of scikit-learn Transformer classes to pipeline and combine features for point-wise ranking modeling and I'd like to convert these features into LibSVM format to experiment with XGBoost and other methods. Is there any easy way to dump scikit-learn features into LibSVM format? Thanks.
I believe you're looking for sklearn.datasets.dump_svmlight_file.
I have two classes and several variables. After training the SVM, it gives me a good accuracy on prediction of testing data classes. Does anybody know how can I find out which of my variables are less important in the prediction done by SVM ? I'm nearly new in SVM and I'm just familiar with console interface and matlab interface of SVM. Is there any option to achive the importance of variables for SVM after training or prediction phase ?
I have trained a SVM (svc) using scikit-learn over half a terabyte of data. The model is working fine and I need to port it to C, but I don't want to re-train the SVM from scratch because it takes way too long for me. Is there a way to easily export the model generated by scikit-learn and import it into LibSVM? Internally scikit-learn uses LibSVM so theoretically it should be possible, but I haven't been able to find anything in the documentation. Any suggestion?
Is there a way to easily export the model generated by scikit-learn and import it into LibSVM?
No. The scikit-learn version of LIBSVM has been hacked up severely to fit it into the Python environment and the model is stored as NumPy/SciPy data structures.
Your best shot is to study the SVM decision function and reimplement it in C. The support vectors can be obtained from the SVC object as NumPy arrays, which are easily translated to C arrays.