What is a CountVectorizer?

The CountVectorizer provides a simple way to both tokenize a collection of text documents and build a vocabulary of known words, but also to encode new documents using that vocabulary. You can use it as follows: Call the fit() function in order to learn a vocabulary from one or more documents.

What does one hot encoding do?

One hot encoding is a process of converting categorical data variables so they can be provided to machine learning algorithms to improve predictions. One hot encoding is a crucial part of feature engineering for machine learning.

What is CountVectorizer in NLP?

CountVectorizer tokenizes(tokenization means breaking down a sentence or paragraph or any text into words) the text along with performing very basic preprocessing like removing the punctuation marks, converting all the words to lowercase, etc.

How is TfidfVectorizer different than CountVectorizer?

TF-IDF is better than Count Vectorizers because it not only focuses on the frequency of words present in the corpus but also provides the importance of the words. We can then remove the words that are less important for analysis, hence making the model building less complex by reducing the input dimensions.

What does Sklearn CountVectorizer do?

Scikit-learn’s CountVectorizer is used to convert a collection of text documents to a vector of term/token counts. It also enables the ​pre-processing of text data prior to generating the vector representation. This functionality makes it a highly flexible feature representation module for text.

What is the difference between one hot encoding and dummies?

One-hot encoding converts it into n variables, while dummy encoding converts it into n-1 variables. If we have k categorical variables, each of which has n values. One hot encoding ends up with kn variables, while dummy encoding ends up with kn-k variables.

Which one is better CountVectorizer or TfidfVectorizer?