Support Vector Machines are powerful algorithms for classification and regression problems from "summary" of Introduction to Machine Learning with Python by Andreas C. Müller,Sarah Guido
Support Vector Machines (SVMs) are a powerful tool for classification and regression tasks in machine learning. They are capable of performing linear or nonlinear classification, regression, and even outlier detection. The underlying principle of SVMs is to find the hyperplane that best separates different classes in the feature space. This hyperplane is chosen in such a way that it maximizes the margin between the classes, making the model more robust and less prone to overfitting.
One of the key advantages of SVMs is their ability to handle high-dimensional data with ease. They work well in cases where the number of features is much larger than the number of samples. SVMs are effective in capturing complex relationships in the data by using kernel functions to map the input features into a higher-dimensional space. This allows SVMs to model nonlinear relationships between the featur...
Read More
Continue reading the Microbook on the Oter App. You can also listen to the highlights by choosing micro or macro audio option on the app. Download now to keep learning!
Now you can listen to your microbooks on-the-go. Download the Oter App on your mobile device and continue making progress towards your goals, no matter where you are.
