Skip to main content

All Questions

Tagged with
Filter by
Sorted by
Tagged with
1 vote
0 answers
40 views

OneClassSVM super slow training with poly kernel

In contrast to questions like here, where a slow SVM training results from a high number of samples, I only have around 500 samples. Still, a single training fold (cross-validation) takes several ...
UserPo41085's user avatar
1 vote
1 answer
41 views

Unexpected behaviour of Scikit-Learn SVR

I'm using Scikit-learn to fit a support vector regression on a really simple dataset of car stopping distances vs car speed. My code for applying SVR to this dataset is: ...
oweydd's user avatar
  • 113
0 votes
1 answer
76 views

why is my svm taking much time to run what changes should i make in my code?

...
Kshitija Thakur's user avatar
1 vote
0 answers
33 views

How does ROC work with SVM?

Could someone please explain how ROC works with SVM? Specifically i'm using RocCurveDisplay.from_predictions(y_test, y_pred, ax=ax[1]) which works fine. Since the ...
lemintare's user avatar
1 vote
2 answers
301 views

Does it make sense to tune a model in scikit-learn and copy/paste the parameters into Rust's linfa?

I have a situation where my data can only be read from in a hosted Python environment, due to data security reasons. However, I am constrained to run ML models in a Rust environment due to work-...
wtwtwt's user avatar
  • 111
0 votes
0 answers
87 views

Issues with sklearn.svm.SVC

I am trying to use the sklearn.svm.SVC on a relatively big dataset, 1.5k test/train samples, 512 features each, one sample per class (so, 1.5k classes). I know that SVC doesn't scale well, so at first ...
Ilya Kuleshov's user avatar
1 vote
0 answers
16 views

Boosting the effect of some of the features in SVM

I'm doing text classification with SVM. I'm using Tfidf vectorization. In addition to the text vectors, I have a context data denoting the possible outcomes of the prediction. For example, I have a ...
cuneyttyler's user avatar
2 votes
2 answers
3k views

Grid_search (RandomizedSearchCV) extremely slow with SVM (SVC)

I'm testing hyperparameters for an SVM, however, when I resort to Gridsearch or RandomizedSearchCV, I haven't been able to get a resolution, because the processing time is exceeding hours. My dataset ...
Paulo Sergio Moreira's user avatar
0 votes
1 answer
43 views

what happens when test data has an instance on the hyperplane. How SVC() classifies it?

What happens when test data has an instance on the hyperplane? How does SVC() of scikit-learn classify it?
AAA's user avatar
  • 35
2 votes
0 answers
81 views

Feature selection and model performance

Featuretools provides an automated way to generate features from your data, by providing relationships within your data and applying their so-called deep feature synthesis. It generates features like ...
holzben's user avatar
  • 121
2 votes
0 answers
670 views

random_state on train_test_split() appears to have large effect in performance metrics?

To summarize the problem: I have a data set with ~1450 samples, 19 features and a binary outcome where classes are fairly balanced (0.51 to 0.49). I split the data into a train set and a test set ...
jlnsci's user avatar
  • 31
3 votes
1 answer
449 views

How do I use wavelet transform for feature extraction correctly?

I'm trying to classify words based on EMG signals using a support vector machine as my model. My dataset includes 15 classes (words) with 230 repetitions and 1000 features each. I already merged all ...
Rose's user avatar
  • 31
2 votes
1 answer
2k views

Imbalanced data set with Sample weighting - How to interpret the performance metrics?

Consider a binary classification scenario whereby the True class (5%) is severely outbalanced to the False class (95%). My data set contains numeric data. I am using SKLearn and trying some different ...
Jurgen Cuschieri's user avatar
6 votes
2 answers
380 views

How sklearn SVM find the initial hyperplane before Optimisation?

The optimization goal of the SVM is to maximize the distance between the positive and negative hyperplanes. But before optimizing, how does sklearn first find the positive and negative support vectors ...
user3363813's user avatar
1 vote
0 answers
58 views

How to handle unclassifiable data in the dataset

Premise: Classification problem Input is three text fields Output classes are A, B, A&B (Note: A and B are not always exclusive though usually are, hence the 'A&B' class) Sci-Kit Learn is the ...
Chor Hatara Hud'u Keturi's user avatar

15 30 50 per page
1
2 3 4 5
8