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Input validation

concept · part of Model security

Validating feature/target splits to avoid unexpected formats that could poison training or break the pipeline.

Input validation also checks for correct data types, missing values, and out-of-range values. For example, a function can verify that input is a pandas DataFrame and contains no missing values, raising a ValueError otherwise.

def validate_input(data):
    if not isinstance(data, pd.DataFrame):
        raise ValueError("Input must be a pandas DataFrame.")
    if data.isnull().values.any():
        raise ValueError("Input data contains missing values.")

Input validation also uses try-except blocks to catch validation errors and print meaningful messages, preventing errors from propagating through the pipeline.

Connections

• Prerequisite of Model security

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