DatasetDescription

Provides comprehensive analysis and statistical summaries of each field in a machine learning model’s dataset.

Purpose

The test depicted in the script is meant to run a comprehensive analysis on a Machine Learning model’s datasets. The test or metric is implemented to obtain a complete summary of the fields in the dataset, including vital statistics of each field such as count, distinct values, missing values, histograms for numerical, categorical, boolean, and text fields. This summary gives a comprehensive overview of the dataset to better understand the characteristics of the data that the model is trained on or evaluates.

Test Mechanism

The DatasetDescription class accomplishes the purpose as follows: firstly, the test method “run” infers the data type of each column in the dataset and stores the details (id, column type). For each field, the describe_dataset_field” method is invoked to collect statistical information about the field, including count, missing value count and its proportion to the total, unique value count, and its proportion to the total. Depending on the data type of a field, histograms are generated that reflect the distribution of data within the field. Numerical fields use the “get_numerical_histograms” method to calculate histogram distribution, whereas for categorical, boolean and text fields, a histogram is computed with frequencies of each unique value in the datasets. For unsupported types, an error is raised. Lastly, a summary table is built to aggregate all the statistical insights and histograms of the fields in a dataset.

Signs of High Risk

• High ratio of missing values to total values in one or more fields which may impact the quality of the predictions.
• Unsupported data types in dataset fields.
• Large number of unique values in the dataset’s fields which might make it harder for the model to establish patterns.
• Extreme skewness or irregular distribution of data as reflected in the histograms.

Strengths

• Provides a detailed analysis of the dataset with versatile summaries like count, unique values, histograms, etc.
• Flexibility in handling different types of data: numerical, categorical, boolean, and text.
• Useful in detecting problems in the dataset like missing values, unsupported data types, irregular data distribution, etc.
• The summary gives a comprehensive understanding of dataset features allowing developers to make informed decisions.

Limitations

• The computation can be expensive from a resource standpoint, particularly for large datasets with numerous fields.
• The histograms use an arbitrary number of bins which may not be the optimal number of bins for specific data distribution.
• Unsupported data types for columns will raise an error which may limit evaluating the dataset.
• Fields with all null or missing values are not included in histogram computation.
• This test only validates the quality of the dataset but doesn’t address the model’s performance directly.