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parameter_estimate_comparisons_chart

At a glance

Useful for: Looking at the m and u value estimates across multiple Splink model training sessions.

API Documentation: parameter_estimate_comparisons_chart()

What is needed to generate the chart? A trained Splink model.

`m u parameters chart`

m u parameters chart

`match weights chart`

match weights chart

Worked Example

```python import splink.comparison_library as cl from splink import DuckDBAPI, Linker, SettingsCreator, block_on, splink_datasets

df = splink_datasets.fake_1000

settings = SettingsCreator( link_type="dedupe_only", comparisons=[ cl.JaroWinklerAtThresholds("first_name", [0.9, 0.7]), cl.JaroAtThresholds("surname", [0.9, 0.7]), cl.DateOfBirthComparison( "dob", input_is_string=True, datetime_metrics=["year", "month"], datetime_thresholds=[1, 1], ), cl.ExactMatch("city").configure(term_frequency_adjustments=True), cl.EmailComparison("email"), ], blocking_rules_to_generate_predictions=[ block_on("first_name"), block_on("surname"), ], )

linker = Linker(df, settings, DuckDBAPI()) linker.training.estimate_u_using_random_sampling(max_pairs=1e6)

blocking_rule_for_training = block_on("first_name", "surname") linker.training.estimate_parameters_using_expectation_maximisation( blocking_rule_for_training )

blocking_rule_for_training = block_on("dob") linker.training.estimate_parameters_using_expectation_maximisation( blocking_rule_for_training )

blocking_rule_for_training = block_on("email") linker.training.estimate_parameters_using_expectation_maximisation( blocking_rule_for_training )

chart = linker.visualisations.parameter_estimate_comparisons_chart() chart

```

```python

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