Зміст курсу
Identifying Spam Emails
Data Preprocessing
CountVectorizer is a feature extraction tool in Natural Language Processing (NLP) that converts a collection of text documents into a matrix of token counts.
It begins by tokenizing the input text, building a vocabulary of known words. It then counts the occurrences of each word in the text and constructs a matrix where each row represents a document, and each column represents a word from the vocabulary.
This matrix can be used as input for various machine learning models to perform text classification, sentiment analysis, and other NLP tasks. Additionally, CountVectorizer can be configured to include preprocessing steps such as removing stopwords and performing stemming or lemmatization.
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- Import the
CountVectorizerclass. - Initialize it and store the instance in the
count_vectorizervariable. - Fit it to the training data (
X_train) using the correct method. - Create the document term matrix using the
.transform()method. - Transform the resulting matrix into an array using the
.toarray()method.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівДякуємо за ваш відгук!
CountVectorizer is a feature extraction tool in Natural Language Processing (NLP) that converts a collection of text documents into a matrix of token counts.
It begins by tokenizing the input text, building a vocabulary of known words. It then counts the occurrences of each word in the text and constructs a matrix where each row represents a document, and each column represents a word from the vocabulary.
This matrix can be used as input for various machine learning models to perform text classification, sentiment analysis, and other NLP tasks. Additionally, CountVectorizer can be configured to include preprocessing steps such as removing stopwords and performing stemming or lemmatization.
Swipe to show code editor
- Import the
CountVectorizerclass. - Initialize it and store the instance in the
count_vectorizervariable. - Fit it to the training data (
X_train) using the correct method. - Create the document term matrix using the
.transform()method. - Transform the resulting matrix into an array using the
.toarray()method.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів