From the course: Python: Working with Predictive Analytics (2019)
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Support Vector Regression (SVR) - Python Tutorial
From the course: Python: Working with Predictive Analytics (2019)
Support Vector Regression (SVR)
- [Instructor] We are again in the modeling section of the roadmap. And now, let's discuss the Support Vector Regression. This is the third model out of five models I'll show you in this course. Imagine a bowling area. Left lane is allowed to use only blue and right lane is only allowed to use green balls. You and your friends just arrived to start your game in the middle lane. That middle lane is acting like a separator between the left and the right lanes. There is a linear separation here. Let's draw that bowling lane-like separation on the graph here. It's main goal is to create an optimal margin which can separate the maximum amount of data points. This method was first used for the classification problems. Then later it was also applied to regression problems to predict numerical data. The plane which separates two classes is called a hyperplane. The data points which are sitting closest to the hyperplane are called the support vectors. The dashed lines are called margins. If we…
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Contents
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Introduction to predictive models2m 52s
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Linear regression6m 25s
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Polynomial regression4m 37s
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Support Vector Regression (SVR)4m 8s
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Decision tree regression5m 43s
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Random forest regression4m 44s
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Evaluation of predictive models2m 56s
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Hyperparameter optimization5m 4s
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Challenge: Hyperparameter optimization1m 15s
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Solution: Hyperparameter optimization6m 55s
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