![]() ![]() In the following, “city” is a categorical attribute while “temperature” Identifiers, types of objects, tags, names…). To a list of discrete possibilities without ordering (e.g. Categoricalįeatures are “attribute-value” pairs where the value is restricted Need not be stored) and storing feature names in addition to values.ĭictVectorizer implements what is called one-of-K or “one-hot”Ĭoding for categorical (aka nominal, discrete) features. While not particularly fast to process, Python’s dict has theĪdvantages of being convenient to use, being sparse (absent features NumPy/SciPy representation used by scikit-learn estimators. The class DictVectorizer can be used to convert featureĪrrays represented as lists of standard Python dict objects to the Is a machine learning technique applied on these features. Images, into numerical features usable for machine learning. The former consists in transforming arbitrary data, such as text or ![]() Feature extraction is very different from Feature selection: ![]()
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