?? Day 156 of 365: Grid Search for Hyperparameter Tuning ??
Ajinkya Deokate
Data Scientist | Researcher | Author | Public Speaking Expert @PlanetSpark | Freelancer
Hey, Team!
Welcome to Day 156 of our #365DaysOfDataScience journey! ??
In this session, we’ll be diving into Grid Search, a powerful tool for systematically tuning hyperparameters. We’ll walk through how to use GridSearchCV to improve our model’s accuracy by finding the best set of hyperparameters.
?? What We’ll Be Exploring Today:
??- Introduction to Grid Search and how it works.
??- Conducting an exhaustive search over a predefined hyperparameter space.
??- Exploring the advantages and limitations of Grid Search.
?? Learning Resources:
??- Read: Scikit-learn documentation on GridSearchCV.
??- Watch: "Grid Search in Machine Learning" (YouTube).
?? Today’s Task:
??- Use GridSearchCV from Scikit-learn to tune the hyperparameters of a Support Vector Machine (SVM) on a dataset (e.g., the Iris dataset).
??- Analyze how the tuned hyperparameters affect the model's performance.
Just like you, I’ll be learning more about how grid search works as I apply it to an SVM model. This should be a fun and insightful step in our journey toward better model performance! Let’s discover how much of an impact good hyperparameter tuning can have.
Happy Learning & See You Soon!
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