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TFIDF

text.TfidfVectorizer([name, ...])

[Beta Version] Create tfidf representation of documents.

Methods:

TfidfVectorizer.contour([nbins, chart])	Draws the model's contour plot.
TfidfVectorizer.deploySQL([X])	Returns the SQL code needed to deploy the model.
TfidfVectorizer.drop()	Drops the model from the VAST DataBase.
TfidfVectorizer.export_models(name, path[, kind])	Exports machine learning models.
TfidfVectorizer.fit(input_relation, index, x)	Applies basic pre-processing.
TfidfVectorizer.get_attributes([attr_name])	Returns the model attributes.
TfidfVectorizer.get_match_index(x, col_list)	Returns the matching index.
TfidfVectorizer.get_params()	Returns the parameters of the model.
TfidfVectorizer.get_plotting_lib([...])	Returns the first available library (Plotly, Matplotlib) to draw a specific graphic.
TfidfVectorizer.import_models(path[, ...])	Imports machine learning models.
TfidfVectorizer.set_params([parameters])	Sets the parameters of the model.
TfidfVectorizer.summarize()	Summarizes the model.
TfidfVectorizer.to_python([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in VAST functions.
TfidfVectorizer.to_sql([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in VAST functions.
TfidfVectorizer.transform(vdf, index, x[, pivot])	Transforms input data to tf-idf representation.

Attributes:

TfidfVectorizer.object_type