SVM for text classification in NLP - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does SVM stand for in machine learning?

SVM stands for Support Vector Machine, a type of algorithm used for classification and regression tasks.

beginner

How does SVM separate different classes in text classification?

SVM finds the best boundary (called a hyperplane) that separates different classes with the largest margin between them.

beginner

Why do we convert text into numbers before using SVM?

Because SVM works with numbers, we convert text into numerical features like word counts or TF-IDF scores to represent the text data.

intermediate

What is the role of the kernel in SVM?

The kernel helps SVM handle data that is not linearly separable by transforming it into a higher-dimensional space where it can be separated.

beginner

What metric is commonly used to evaluate SVM performance in text classification?

Accuracy is commonly used, but precision, recall, and F1-score are also important to understand how well the SVM classifies text.

What is the main goal of SVM in text classification?

A To count the number of words in text

B To find the best boundary that separates classes

C To translate text into another language

D To generate new text data

SVM aims to find the best boundary (hyperplane) that separates different classes with the largest margin.

Which step is necessary before applying SVM to text data?

A Sorting words alphabetically

B Translating text to images

C Removing all vowels from text

D Converting text into numerical features

Text must be converted into numbers like word counts or TF-IDF scores for SVM to process it.

What does the kernel function in SVM do?

A Calculates word frequency

B Removes stop words from text

C Transforms data to a higher dimension to separate classes

D Splits data into training and testing sets

The kernel transforms data so that SVM can separate classes that are not linearly separable.

Which metric is NOT typically used to evaluate SVM text classification?

A Page load time

B Recall

C F1-score

D Accuracy

Page load time is unrelated to evaluating SVM models.

What does a large margin in SVM mean?

A Better separation between classes

B More words in the text

C Longer training time

D More errors in classification

A large margin means the classes are well separated, which usually improves model performance.

Explain how SVM works for text classification from raw text to prediction.

Think about how text turns into numbers and how SVM separates classes.

Describe why kernels are important in SVM for text classification.

Consider cases when data cannot be separated by a straight line.