kernelized support vector machines are powerful models and perform well on variety of datasets svms allow for complex decision boundarieseven if the data has only few features they work well on low-dimensional and high-dimensional data ( few and many features)but don' scale very well with the number of samples running an svm on data with up to , samples might work wellbut working with datasets of size , or more can become challenging in terms of runtime and memory usage another downside of svms is that they require careful preprocessing of the data and tuning of the parameters this is whythese daysmost people instead use tree-based models such as random forests or gradient boosting (which require little or no preprocessingin many applications furthermoresvm models are hard to inspectit can be difficult to understand why particular prediction was madeand it might be tricky to explain the model to nonexpert stillit might be worth trying svmsparticularly if all of your features represent measurements in similar units ( all are pixel intensitiesand they are on similar scales the important parameters in kernel svms are the regularization parameter cthe choice of the kerneland the kernel-specific parameters although we primarily focused on the rbf kernelother choices are available in scikit-learn the rbf kernel has only one parametergammawhich is the inverse of the width of the gaussian kernel gamma and both control the complexity of the modelwith large values in either resulting in more complex model thereforegood settings for the two parameters are usually strongly correlatedand and gamma should be adjusted together neural networks (deep learninga family of algorithms known as neural networks has recently seen revival under the name "deep learning while deep learning shows great promise in many machine learning applicationsdeep learning algorithms are often tailored very carefully to specific use case herewe will only discuss some relatively simple methodsnamely multilayer perceptrons for classification and regressionthat can serve as starting point for more involved deep learning methods multilayer perceptrons (mlpsare also known as (vanillafeed-forward neural networksor sometimes just neural networks the neural network model mlps can be viewed as generalizations of linear models that perform multiple stages of processing to come to decision supervised learning