DOC change "cumulative density" to "cumulative distribution"
@@ -1997,7 +1997,7 @@ class QuantileTransformer(BaseEstimator, TransformerMixin):
(marginal) outliers: this is therefore a robust preprocessing scheme.
The transformation is applied on each feature independently.
- The cumulative density function of a feature is used to project the
+ The cumulative distribution function of a feature is used to project the
original values. Features values of new/unseen data that fall below
or above the fitted range will be mapped to the bounds of the output
distribution. Note that this transform is non-linear. It may distort linear
@@ -2010,7 +2010,7 @@ class QuantileTransformer(BaseEstimator, TransformerMixin):
----------
n_quantiles : int, optional (default=1000)
Number of quantiles to be computed. It corresponds to the number
- of landmarks used to discretize the cumulative density function.
+ of landmarks used to discretize the cumulative distribution function.
output_distribution : str, optional (default='uniform')
Marginal distribution for the transformed data. The choices are
@@ -2387,7 +2387,7 @@ def quantile_transform(X, axis=0, n_quantiles=1000,
(marginal) outliers: this is therefore a robust preprocessing scheme.
The transformation is applied on each feature independently.
- The cumulative density function of a feature is used to project the
+ The cumulative distribution function of a feature is used to project the
original values. Features values of new/unseen data that fall below
or above the fitted range will be mapped to the bounds of the output
distribution. Note that this transform is non-linear. It may distort linear
@@ -2407,7 +2407,7 @@ def quantile_transform(X, axis=0, n_quantiles=1000,
n_quantiles : int, optional (default=1000)
Number of quantiles to be computed. It corresponds to the number
- of landmarks used to discretize the cumulative density function.
+ of landmarks used to discretize the cumulative distribution function.
output_distribution : str, optional (default='uniform')
Marginal distribution for the transformed data. The choices are
