ParthivNaresh
ParthivNaresh
Currently `visualize_decision_tree` supports only Decision Trees. We can expand the functionality to include support for Pipelines (so that the associated estimator can be taken and visualized) and for Random Forests....
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`ignore_columns` has been added as an argument when initializing a dataframe with woodwork. Any column name(s) that are passed in to `df.ww.init(ignore_columns=[col_1, col_2, etc])` will be ignored during initialization, as...
Currently a column of floats ending in 0 such as `[1.0, 3.0, 12.0, etc]` is inferred as an `Integer` ltype or `IntegerNullable` if there are null values in it. This...
Currently if a user attempts to assign multiple columns using the woodwork accessor, an error is thrown. ``` def test_multiple_assignment(): df = pd.DataFrame() df["ints"] = [i for i in range(100)]...
Today all categorical columns are included in the calculation of mutual information scores. This is normally fine except when the number of unique categories in the categorical column increases to...
Currently calling `df.ww.init()` infers the logical types and performs transformations as well. Doing the same for `series.ww.init()` only infers the logical type, but doesn't change the underlying `dtype` or transform...
We've made a series of changes to Woodwork in the 0.17.0 release regarding how nullables are handled and represented. We should update the docs to reflect this.
If a series is passed in with the following types of values, we should expect an inference of Boolean or BooleanNullable: - All 0s, 1s, or a combination of 0s...