RowDataSource

Data source for explainability analysis using a single row of data. RowDataSource allows you to perform explainability analysis on a specific data row by providing the row data directly. This is useful when you want to explain a particular prediction or analyze feature importance for a specific instance without referencing stored data. This data source type is ideal for real-time explanations, ad-hoc analysis, or when you have specific data points that you want to analyze independently of your stored datasets.

Examples

Creating a row data source for a loan application:

row_source = RowDataSource(
    row={
        "age": 35,
        "income": 75000,
        "credit_score": 720,
        "employment_years": 8,
        "loan_amount": 250000
    }
)

Creating a row data source for image classification:

image_row_source = RowDataSource(
    row={
        "image_features": [0.1, 0.5, 0.3, ...],
        "metadata": "product_image_001.jpg",
        "category": "electronics"
    }
)

source_type

row

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

Multivariate

TextEmbedding

⌘I

Fiddler Python Client SDK

Fiddler Evals SDK

Fiddler OTel SDK

Fiddler LangGraph SDK

Fiddler LangChain SDK

Fiddler Google ADK SDK

Fiddler Strands Agent SDK

Fiddler OTel JS SDK

Fiddler LangGraph JS SDK

Fiddler LangChain JS SDK

Examples

source_type

row

model_config

​Examples

​source_type

​row

​model_config

Examples

source_type

row

model_config