This article was written by Sebastian Raschka.
Given an arbitrary dataset, you typically don’t know which kernel may work best. I recommend starting with the simplest hypothesis space first — given that you don’t know much about your data — and work your way up towards the more complex hypothesis spaces. So, the linear kernel works fine if your dataset if linearly separable; however, if your dataset isn’t linearly separable, a linear kernel isn’t going to cut it (almost in a literal sense).
For simplicity (and visualization purposes), let’s assume our dataset consists of 2 dimensions only. Below, I plotted the decision regions of a linear SVM on 2 features of the iris dataset.
To read the rest of the article, click here.
DSC Ressources
- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Deep Analytical Thinking and Data Science Wizardry
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions
Follow us: Twitter | Facebook
Credit: Data Science Central By: Andrea Manero-Bastin
