✍🏻 Modify type docstring

Author: EssamWisam

src/encoders/missingness_encoding/errors.jl

@@ -1,7 +1,7 @@
 UNSUPPORTED_COL_TYPE_ME(col_type) =
-    "In MissingnessEncoder, elements have type $(col_type). The supported types are `Union{Char, AbstractString, Number}`"
+    "In MissingnessEncoder, elements have type $(col_type). The supported types are `Char`, `AbstractString`, and `Number`"
 VALID_TYPES_NEW_VAL_ME(possible_col_type) =
-    "In MissingnessEncoder, label_for_missing keys have type $(possible_col_type). The supported types are `Union{Char, AbstractString, Number}`"
+    "In MissingnessEncoder, label_for_missing keys have type $(possible_col_type). The supported types are `Char`, `AbstractString`, and `Number`"
 COLLISION_NEW_VAL_ME(value) =
     "In MissingnessEncoder, label_for_missing specifies new feature name $(value). However, this name already exists in one of the features. Please respecify label_for_missing."
 UNSPECIFIED_COL_TYPE_ME(col_type, label_for_missing) =

src/encoders/missingness_encoding/interface_mlj.jl

@@ -82,7 +82,7 @@ $(MMI.doc_header(MissingnessEncoder))
 `MissingnessEncoder` maps any missing level of a categorical feature into a new level (e.g., "Missing"). 
 By this, missingness will be treated as a new
 level by any subsequent model. This assumes that the categorical features have raw
-types that are in `Union{Char, AbstractString, Number}`.
+types that are in `Char`, `AbstractString`, and `Number`.
 
 # Training data
 
@@ -104,7 +104,7 @@ Train the machine using `fit!(mach, rows=...)`.
 - `ignore=true`: Whether to exclude or includes the features given in `features`
 - `ordered_factor=false`: Whether to encode `OrderedFactor` or ignore them
 - `label_for_missing::Dict{<:Type, <:Any}()= Dict( AbstractString => "missing", Char => 'm', )`: A
-dictionary where the possible values for keys are the types in `Union{Char, AbstractString, Number}` and where each value
+dictionary where the possible values for keys are the types in `Char`, `AbstractString`, and `Number` and where each value
 signifies the new level to map into given a column raw super type. By default, if the raw type of the column subtypes `AbstractString`
 then missing values will be replaced with `"missing"` and if the raw type subtypes `Char` then the new value is `'m'`
 and if the raw type subtypes `Number` then the new value is the lowest value in the column - 1.

src/encoders/missingness_encoding/missingness_encoding.jl

@@ -5,7 +5,7 @@ include("errors.jl")
 
 Fit a transformer that maps any missing value into a new level (e.g., "Missing"). By this, missingness will be treated as a new
 level by any subsequent model. This assumes that the categorical features have raw
-types that are in `Union{Char, AbstractString, Number}`.
+types that are in `Char`, `AbstractString`, and `Number`.
 
 # Arguments
 
@@ -15,7 +15,7 @@ types that are in `Union{Char, AbstractString, Number}`.
   - `ignore=true`: Whether to exclude or includes the features given in `features`
   - `ordered_factor=false`: Whether to encode `OrderedFactor` or ignore them
   - `label_for_missing::Dict{<:Type, <:Any}()= Dict( AbstractString => "missing", Char => 'm', )`: A
-    dictionary where the possible values for keys are the types in `Union{Char, AbstractString, Number}` and where each value
+    dictionary where the possible values for keys are the types in `Char`, `AbstractString`, and `Number` and where each value
     signifies the new level to map into given a column raw super type. By default, if the raw type of the column subtypes `AbstractString`
     then missing values will be replaced with `"missing"` and if the raw type subtypes `Char` then the new value is `'m'`
     and if the raw type subtypes `Number` then the new value is the lowest value in the column - 1.

src/transformers/cardinality_reducer/cardinality_reducer.jl

@@ -7,7 +7,7 @@ include("errors.jl")
 Fit a transformer that maps any level of a categorical feature that occurs with
 frequency < `min_frequency` into a new level (e.g., "Other"). This is useful when some categorical features have
 high cardinality and many levels are infrequent. This assumes that the categorical features have raw
-types that are in `Union{Char, AbstractString, Number}`.
+types that are in `Char`, `AbstractString`, and `Number`.
 
 # Arguments
 
@@ -19,7 +19,7 @@ types that are in `Union{Char, AbstractString, Number}`.
   - `min_frequency::Real=3`: Any level of a categorical feature that occurs with frequency < `min_frequency` will be mapped to a new level. Could be
     an integer or a float which decides whether raw counts or normalized frequencies are used.
   - `label_for_infrequent::Dict{<:Type, <:Any}()= Dict( AbstractString => "Other", Char => 'O', )`: A
-    dictionary where the possible values for keys are the types in `Union{Char, AbstractString, Number}` and each value signifies
+    dictionary where the possible values for keys are the types in `Char`, `AbstractString`, and `Number` and each value signifies
     the new level to map into given a column raw super type. By default, if the raw type of the column subtypes `AbstractString`
     then the new value is `"Other"` and if the raw type subtypes `Char` then the new value is `'O'`
     and if the raw type subtypes `Number` then the new value is the lowest value in the column - 1.

src/transformers/cardinality_reducer/interface_mlj.jl

@@ -112,7 +112,7 @@ Train the machine using `fit!(mach, rows=...)`.
 - `min_frequency::Real=3`: Any level of a categorical feature that occurs with frequency < `min_frequency` will be mapped to a new level. Could be
 an integer or a float which decides whether raw counts or normalized frequencies are used.
 - `label_for_infrequent::Dict{<:Type, <:Any}()= Dict( AbstractString => "Other", Char => 'O', )`: A
-dictionary where the possible values for keys are the types in `Union{Char, AbstractString, Number}` and each value signifies
+dictionary where the possible values for keys are the types in `Char`, `AbstractString`, and `Number` and each value signifies
 the new level to map into given a column raw super type. By default, if the raw type of the column subtypes `AbstractString`
 then the new value is `"Other"` and if the raw type subtypes `Char` then the new value is `'O'`
 and if the raw type subtypes `Number` then the new value is the lowest value in the column - 1.