Introduction to Support Vector Machines (SVM) and hands-on practice
Introduction to Support Vector Machines (SVM) and hands-on practice Data Science Project
Classification in Depth with Scikit-Learn

Introduction to Support Vector Machines (SVM) and hands-on practice

In this project, you'll learn about a classical machine learning model called 'Support Vector Machine' (SVM). This is a linear model that can be used for regression and classification tasks. You will also learn about kernel tricks and how to implement this model using Scikit-learn.
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Project Activities

All our Data Science projects include bite-sized activities to test your knowledge and practice in an environment with constant feedback.

All our activities include solutions with explanations on how they work and why we chose them.

codevalidated

Train and test split

Use train_test_split to split the data into training and testing sets. Split the dataset in 80% training, 20% testing, and random_state=0.

Store the values in the variables in X_train,X_test,y_train, y_test,random_state .

multiplechoice

SVM can perform well without normalization, even when input features have vastly different scales. True or False?

multiplechoice

Normalization is only necessary for linear SVM, and not necessary for kernel SVM. True or False?

multiplechoice

Based on the figure below, Which SVM classifier is likely to suffer from overfitting?

c

multiplechoice

Based on the figure below, Which SVM classifier is likely to suffer from underfitting?

c

Introduction to Support Vector Machines (SVM) and hands-on practiceIntroduction to Support Vector Machines (SVM) and hands-on practice
Author

Verónica Barraza

This project is part of

Classification in Depth with Scikit-Learn

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