class_docstr: A Pipeline expression computing a categorical output.
  
  Classifiers are most commonly useful for describing grouping keys for
  complex transformations on Factor outputs. For example, Factor.demean() and
  Factor.zscore() can be passed a Classifier in their ``groupby`` argument,
  indicating that means/standard deviations should be computed on assets for
  which the classifier produced the same label.
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code: def _aliased_type(self):
      return AliasedMixin.make_aliased_type(Classifier)
  
imports: from numbers import Number
  import operator
  import re
  from numpy import where, isnan, nan, zeros
  import pandas
  from catalyst.lib.labelarray import LabelArray
  from catalyst.lib.quantiles import quantiles
  from catalyst.pipeline.api_utils import restrict_to_dtype
  from catalyst.pipeline.sentinels import NotSpecified
  from catalyst.pipeline.term import ComputableTerm
  from catalyst.utils.compat import unicode
  from catalyst.utils.input_validation import expect_types, expect_dtypes
  from catalyst.utils.memoize import classlazyval
  from catalyst.utils.numpy_utils import categorical_dtype, int64_dtype, vectorized_is_element
  from ..filters import ArrayPredicate, NotNullFilter, NullFilter, NumExprFilter
  from ..mixins import AliasedMixin, CustomTermMixin, DownsampledMixin, LatestMixin, PositiveWindowLengthMixin, RestrictedDTypeMixin, SingleInputMixin, StandardOutputs
label_desc: Use image node_id 14 for calling the Classifier obj's underlying member method code with example usage: obj._aliased_type() and returns: AliasedMixin