Problem Types¶

Submodules¶

Enum defining the supported types of machine learning problems.

`detect_problem_type`	Determine the type of problem is being solved based on the targets (binary vs multiclass classification, regression)
`handle_problem_types`	Handles problem_type by either returning the ProblemTypes or converting from a str.
`is_binary`	Determines if the provided problem_type is a binary classification problem type
`is_classification`	Determines if the provided problem_type is a classification problem type
`is_multiclass`	Determines if the provided problem_type is a multiclass classification problem type
`is_regression`	Determines if the provided problem_type is a regression problem type
`is_time_series`	Determines if the provided problem_type is a time series problem type

evalml.problem_types.detect_problem_type(y)[source]¶

Determine the type of problem is being solved based on the targets (binary vs multiclass classification, regression): Ignores missing and null data

Example

>>> y = pd.Series([0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1])
>>> problem_type = detect_problem_type(y)
>>> assert problem_type == ProblemTypes.BINARY

evalml.problem_types.handle_problem_types(problem_type)[source]¶

Handles problem_type by either returning the ProblemTypes or converting from a str.

Parameters: problem_type (str or ProblemTypes) – Problem type that needs to be handled
Returns: ProblemTypes

evalml.problem_types.is_binary(problem_type)[source]¶

Determines if the provided problem_type is a binary classification problem type

Parameters: problem_type (str or ProblemTypes) – type of supervised learning problem. See evalml.problem_types.ProblemType.all_problem_types for a full list.
Returns: Whether or not the provided problem_type is a binary classification problem type.
Return type: bool

evalml.problem_types.is_classification(problem_type)[source]¶

Determines if the provided problem_type is a classification problem type

Parameters: problem_type (str or ProblemTypes) – type of supervised learning problem. See evalml.problem_types.ProblemType.all_problem_types for a full list.
Returns: Whether or not the provided problem_type is a classification problem type.
Return type: bool

evalml.problem_types.is_multiclass(problem_type)[source]¶

Determines if the provided problem_type is a multiclass classification problem type

Parameters: problem_type (str or ProblemTypes) – type of supervised learning problem. See evalml.problem_types.ProblemType.all_problem_types for a full list.
Returns: Whether or not the provided problem_type is a multiclass classification problem type.
Return type: bool

evalml.problem_types.is_regression(problem_type)[source]¶

Determines if the provided problem_type is a regression problem type

Parameters: problem_type (str or ProblemTypes) – type of supervised learning problem. See evalml.problem_types.ProblemType.all_problem_types for a full list.
Returns: Whether or not the provided problem_type is a regression problem type.
Return type: bool

evalml.problem_types.is_time_series(problem_type)[source]¶

Determines if the provided problem_type is a time series problem type

Parameters: problem_type (str or ProblemTypes) – type of supervised learning problem. See evalml.problem_types.ProblemType.all_problem_types for a full list.
Returns: Whether or not the provided problem_type is a time series problem type.
Return type: bool

class evalml.problem_types.ProblemTypes[source]¶

Enum defining the supported types of machine learning problems.

Attributes

BINARY	Binary classification problem.
MULTICLASS	Multiclass classification problem.
REGRESSION	Regression problem.
TIME_SERIES_BINARY	Time series binary classification problem.
TIME_SERIES_MULTICLASS	Time series multiclass classification problem.
TIME_SERIES_REGRESSION	Time series regression problem.

Methods

`all_problem_types`	Get a list of all defined problem types.
`name`	The name of the Enum member.
`value`	The value of the Enum member.

all_problem_types(cls)¶

Get a list of all defined problem types.

utils problem_types