Support Vector Machine (SVM) Hyperparameter Tuning In Python | Machine Learning
Support Vector Machine (SVM) is a supervised machine learning model for classifications and regressions. Since SVM is commonly used for classification, we will use the classification model as an example in this tutorial. We will cover:
👉 What's the intuition for the Support Vector Machine (SVM) algorithm?
👉 What are the most critical hyperparameters for Support Vector Machine (SVM)?
👉 How to tune hyperparameters for Support Vector Machine (SVM) using grid search, random search, and Bayesian optimization?
⏰ Timecodes ⏰
0:00 - Intro
1:06 - Step 1: Support Vector Machine (SVM) algorithm
2:17 - Step 2: Support Vector Machine (SVM) Hyperparameters
4:36 - Step 3: Import Libraries
5:17 - Step 4: Read Data
5:43 - Step 5: Train Test Split
6:01 - Step 6: Standardization
7:16 - Step 7: Support Vector Machine (SVM) Default Hyperparameters
7:42 - Step 8: Hyperparameter Tuning Using Grid Search
10:38 - Step 9: Hyperparameter Tuning Using Random Search
12:12 - Step 10: Hyperparameter Tuning Using Bayesian Optimization
13:58 - Summary
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