clarify in docs the meaning of "table" to close #971

docs/src/about_mlj.md

@@ -143,8 +143,9 @@ Extract:
 
 ## Key features
 
-* Data agnostic, train models on any data supported by the
-  [Tables.jl](https://github.com/JuliaData/Tables.jl) interface.
+* Data agnostic, train most models on any data `X` supported by the
+  [Tables.jl](https://github.com/JuliaData/Tables.jl) interface (needs `Tables.istable(X)
+  == true`).
 
 * Extensive, state-of-the-art, support for model composition
   (*pipelines*, *stacks* and, more generally, *learning networks*). See more

docs/src/common_mlj_workflows.md

@@ -77,7 +77,7 @@ Loading a built-in data set already split into `X` and `y`:
 
 ```@example workflows
 X, y = @load_iris;
-selectrows(X, 1:4) # selectrows works for any Tables.jl table
+selectrows(X, 1:4) # selectrows works whenever `Tables.istable(X)==true`.
 ```
 
 ```@example workflows

docs/src/getting_started.md

@@ -33,12 +33,11 @@ schema(iris)
 ```
 
 Because this data format is compatible with
-[Tables.jl](https://tables.juliadata.org/stable/), many MLJ methods
-(such as `selectrows`, `pretty` and `schema` used above) as well as
-many MLJ models can work with it. However, as most new users are
-already familiar with the access methods particular to
-[DataFrames](https://dataframes.juliadata.org/stable/) (also
-compatible with Tables.jl) we'll put our data into that format here:
+[Tables.jl](https://tables.juliadata.org/stable/) (and satisfies `Tables.istable(iris) ==
+true`) many MLJ methods (such as `selectrows`, `pretty` and `schema` used above) as well
+as many MLJ models can work with it. However, as most new users are already familiar with
+the access methods particular to [DataFrames](https://dataframes.juliadata.org/stable/)
+(also compatible with Tables.jl) we'll put our data into that format here:
 
 ```@example doda
 import DataFrames
@@ -334,14 +333,12 @@ scitype(X)
 
 ### Two-dimensional data
 
-Generally, two-dimensional data in MLJ is expected to be *tabular*.
-All data containers compatible with the
-[Tables.jl](https://github.com/JuliaData/Tables.jl) interface (which
-includes all source formats listed
-[here](https://github.com/JuliaData/Tables.jl/blob/master/INTEGRATIONS.md))
-have the scientific type `Table{K}`, where `K` depends on the
-scientific types of the columns, which can be individually inspected
-using `schema`:
+Generally, two-dimensional data in MLJ is expected to be *tabular*.  All data containers
+`X` compatible with the [Tables.jl](https://github.com/JuliaData/Tables.jl) interface and
+sastisfying `Tables.istable(X) == true` (most of the formats in [this
+list](https://github.com/JuliaData/Tables.jl/blob/master/INTEGRATIONS.md)) have the
+scientific type `Table{K}`, where `K` depends on the scientific types of the columns,
+which can be individually inspected using `schema`:
 
 ```@repl doda
 schema(X)
@@ -385,10 +382,10 @@ resampling is always more efficient in this case.
 
 ### Inputs
 
-Since an MLJ model only specifies the scientific type of data, if that
-type is `Table` - which is the case for the majority of MLJ models -
-then any [Tables.jl](https://github.com/JuliaData/Tables.jl) format is
-permitted.
+Since an MLJ model only specifies the scientific type of data, if that type is `Table` -
+which is the case for the majority of MLJ models - then any
+[Tables.jl](https://github.com/JuliaData/Tables.jl) container `X` is permitted, so long as
+`Tables.istable(X) == true`.
 
 Specifically, the requirement for an arbitrary model's input is `scitype(X)
 <: input_scitype(model)`.

docs/src/quick_start_guide_to_adding_models.md

@@ -12,11 +12,10 @@ of how things work with MLJ.  In particular, you are familiar with:
 
 - what `Probabilistic`, `Deterministic` and `Unsupervised` models are
 
-- the fact that MLJ generally works with tables rather than
-  matrices. Here a *table* is a container satisfying the
-  [Tables.jl](https://github.com/JuliaData/Tables.jl) API (e.g.,
-  DataFrame, JuliaDB table, CSV file, named tuple of equal-length
-  vectors)
+- the fact that MLJ generally works with tables rather than matrices. Here a *table* is a
+  container `X` satisfying the [Tables.jl](https://github.com/JuliaData/Tables.jl) API and
+  satisifying `Tables.istable(X) == true` (e.g., DataFrame, JuliaDB table, CSV file, named
+  tuple of equal-length vectors)
 
 - [CategoricalArrays.jl](https://github.com/JuliaData/CategoricalArrays.jl)
   (if working with finite discrete data, e.g., doing classification)