# Optimisation of SVM parameters **URL:** \ **Category:** KNIME Analytics Platform\ **Created:** [August 4, 2017, 2:19pm UTC](https://forum.knime.com/t/optimisation-of-svm-parameters/9167 "2017-08-04T14:19:56Z")\ **Posts on this page:** 1\ **Showing post:** 24
**Author:** ![mlauber71](https://forum-cdn.knime.com/user_avatar/forum.knime.com/mlauber71/32/73590_2.png) [@mlauber71](https://forum.knime.com/u/mlauber71)\ **Post date:** [December 4, 2022, 8:03am UTC](https://forum.knime.com/t/optimisation-of-svm-parameters/9167/24 "2022-12-04T08:03:50Z")
I am having a bit of a deja vu here 🙂 since I tried to build a [workflow comparing several multiclass models](https://hub.knime.com/-/spaces/-/latest/~m4XD6wQhbUsoa6j1/) with seemingly similar data before and also discussed about the structure and quality of the data: > [@Help with SVM algorithm (under optimization loop)](https://forum.knime.com/t/help-with-svm-algorithm-under-optimization-loop/48070/5): > > @rogerius1st SMO indeed has the best value in a quick comparison. This might not say that much. You might want to think about reformulating your question as a regression task? Would that be suitable to your needs? [image] The Accuracy still is not good. You might be able to tweak this by working on the parameters for the SMO but with your small dataset that might run the risk of overfitting. [image] My impression then was that it had more to do with the task and also the target. I suggested if maybe formulating it as a regression task might help but we never managed to finish that conversation. I wonder if given more data an SVM might perform better with a regression task. A support vector classifier came out on top the last time around although the model overall was not very good. Other relevant links might be this. > [@How to evaluate multiple (classification) models and choose the best one? (without doing too much manual work)](https://forum.knime.com/t/how-to-evaluate-multiple-classification-models-and-choose-the-best-one-without-doing-too-much-manual-work/3277/5): > > @rogerius1st I see these approaches (besides it would be better to open a new thread and leave such old ones alone). First- AutoML Regression and Classification Examples. Like @Daniel_Weikert has mentioned these are the components currently being developed by KNIME: [AutoML Regression and Classification Examples – KNIME Community Hub](https://kni.me/w/ifIPg5Pr0hCk62GS) (example on the hub for classification and regression) [Guided Automation – KNIME Community Hub](https://kni.me/w/eAGfGtEAIr-1iYR-) (guided automation) [Compute and Visualize Global Feature Importan…](https://kni.me/w/NyMrzvzPx35QN2Gi) > [@Help with multi-label classification](https://forum.knime.com/t/help-with-multi-label-classification/46881/2): > > @rogerius1st you could check the links about multiclass in my machine learning collection And then these sample workflows: [https://hub.knime.com/search?type=Workflow&tag=multiclass,h2o.ai&sort=maxKudos](https://hub.knime.com/search?type=Workflow&tag=multiclass,h2o.ai&sort=maxKudos) A good statistic to go for might be LogLoss: --- _[View the full topic](https://forum.knime.com/t/optimisation-of-svm-parameters/9167)._