???? Day 180 of 365: Hyperparameter Tuning for SVMs ????
Ajinkya Deokate
Data Scientist | Researcher | Author | Public Speaking Expert @PlanetSpark | Freelancer
Hey, Tuner!
Welcome to Day 180 of our #365DaysOfDataScience journey! ??
Let’s get optimizing! This step will help refine our understanding of SVMs and teach us how much hyperparameters can affect performance. Happy tuning!
???? Today's Goal:
- Understanding key hyperparameters of SVMs:??
??- C: Controls the trade-off between a smooth decision boundary and classifying training points correctly.
??- Kernel: Linear, polynomial, RBF, or custom kernels for different data types.
??- Gamma: Defines how far the influence of a single training example reaches (applicable for RBF and polynomial kernels).
?? Learning Resources:
- Read: Scikit-learn documentation on GridSearchCV.
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?? Today’s Task:
- Today, we’ll be optimizing our SVM model by fine-tuning these key hyperparameters using GridSearchCV. This will help us automate the search for the best parameters and improve our model's performance.
- We'll take a dataset like the Iris dataset, or any one you’ve been working with, and run GridSearchCV to optimize the values of C, the kernel type, and gamma.?
- Compare the model’s performance with and without tuning, and document your findings.
Happy Learning & See You Soon!