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Why is my SVM taking so long?
The most likely explanation is that you’re using too many training examples for your SVM implementation. SVMs are based around a kernel function. Most implementations explicitly store this as an NxN matrix of distances between the training points to avoid computing entries over and over again.
What is N_jobs Sklearn?
n_jobs is an integer, specifying the maximum number of concurrently running workers. If 1 is given, no joblib parallelism is used at all, which is useful for debugging. If set to -1, all CPUs are used. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. For example with n_jobs=-2, all CPUs but one are used.
Why is SVM bad?
Some Cons, SVM algorithm is not suitable for large data sets,SVM does not perform very well, when the data set has more noise i.e. target classes are overlapping. Some Pros, SVM works relatively well when there is clear margin of separation between classes and SVM is more effective in high dimensional spaces.
How long does it take to train SVM classifier in Python?
Where X, y (X – 30000×784 matrix, y – 30000×1) are numpy arrays. On small data algorithm works well and give me right results. But I run my program about 10 hours ago… And it is still in process. I want to know how long it will take, or it stuck in some way? (Laptop specs 4 GB Memory, Core i5-480M)
Why does training an SVM take so long?
However, the training processes of the various SVMs take much too long. Is this reasonable? I have also examined the ensemble BaggingClassifier in combination with non-linear SVMs, by configuring the n_jobs parameter to -1; nevertheless, the training process proceeds again too slowly. How can I speed up the training processes?
How to approximate a SVM with scikit learn?
You can subsample the data and use the rest as a validation set, or you can pick a different model. Above the 200,000 observation range, it’s wise to choose linear learners. Kernel SVM can be approximated, by approximating the kernel matrix and feeding it to a linear SVM.
Can a kernel SVM be approximated in Python?
Kernel SVM can be approximated, by approximating the kernel matrix and feeding it to a linear SVM. This allows you to trade off between accuracy and performance in linear time. A popular means of achieving this is to use 100 or so cluster centers found by kmeans/kmeans++ as the basis of your kernel function.