How do you use TfidfVectorizer in Scikit-learn to transform text data for a classification task

Question

With the help of proper code can you tell me How do you use TfidfVectorizer in Scikit-learn to transform text data for a classification task?

Answer 1

You can transform text data for classification using TfidfVectorizer in Scikit-learn to convert text into numerical features based on term frequency-inverse document frequency.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• TfidfVectorizer() converts text data into a numerical matrix of TF-IDF features.
• fit_transform(texts) learns the vocabulary and transforms the text data into a feature matrix.
• Logistic regression is used to classify the transformed text data.
• accuracy_score() evaluates model performance on test data.

Hence, TfidfVectorizer effectively transforms text into meaningful numerical features, enabling traditional machine learning models to perform classification tasks on text data.