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tf–idf (term frequency–inverse document frequency), is a numerical statistic using in nlp that is intended to reflect how important a word is to a document in a collection or corpus. It is often used as a weighting factor in searches of information retrieval, text mining, and user modeling. tf–idf increases proportionally the number of times a word appears in the document.

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How to choose the best parameter values for TfidfVectorizer in sklearn library?

Recently, I used TfidfVectorizer in scikit-learn library to calculate a matrix of TF-IDF features. However, I do not know how to set some parameters such as max_features, min_df, max_df, etc.
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