New! Use Simple ML for Sheets to apply machine learning to the data in your Google Sheets
Read More
tfdf.py_tree.condition.AbstractCondition
Stay organized with collections
Save and categorize content based on your preferences.
Generic condition.
tfdf.py_tree.condition.AbstractCondition(
missing_evaluation: Optional[bool], split_score: Optional[float] = None
)
Attributes |
|---|
missing_evaluation
|
Result of the evaluation of the condition if the feature
is missing. If None, a feature cannot be missing or a specific method run
during inference to handle missing values.
|
split_score
|
|
Methods
features
View source
@abc.abstractmethod
features() -> List[tfdf.inspector.SimpleColumnSpec]
List of features used to evaluate the condition.
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2024-04-26 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26 UTC."],[],[]]
View source on GitHub