I am working with a data-set of around ~100000 observations(rows) and ~256 features(columns). Is there any recommendation for applying Machine Learning techniques on such a data-set efficiently ? Maybe by parallelization or similar approaches ?
I am currently using Matlab for applying different Machine Learning, but have investigated Python's scikit-learn as well for applying:
- Gaussian processes for regression GPR
- Linear discriminant analysis LDA
- Support vector machine SVM
Obviously dimentionality reduction comes to mind, however for this specific data-set removing some of the features or applying transformations will distort the information.