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Since a simple modulo is used to transform the hash function to a column index, That the sign bit of MurmurHash3 is independent of its other bits. The present implementation works under the assumption To determine the column index and sign of a feature, respectively. Used two separate hash functions \(h\) and \(\xi\) The original formulation of the hashing trick by Weinberger et al. In the following, “city” is a categorical attribute while “temperature” Identifiers, types of objects, tags, names…). To a list of discrete of possibilities without ordering (e.g.

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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.

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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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