In the context of the project “https://github.com/moj-analytical-services/splink_demos/”, training refers to the process of estimating linkage probabilities and model parameters. This is a crucial step in the record linkage process, where the goal is to identify and link records that refer to the same entities across different data sources.
There are several techniques and options available for training a record linkage model in this project. Here are some of the main options, along with examples and source references:
- Using the
splink.train()function: This is the main function for training a Splink model. It takes ablock_matrixobject as input, which represents the pairwise comparisons between records in the input datasets. The function estimates the linkage probabilities and model parameters based on the input data and the specified settings.
Example:
from splink.training import train
block_matrix = # create block matrix object
model_params = train(block_matrix)
- Specifying training settings: There are various settings and parameters that can be specified during the training process, such as the prior probabilities, the number of threads, and the convergence threshold. These settings can be specified as arguments to the
train()function.
Example:
model_params = train(block_matrix,
prior_prob_estimated_positives=0.01,
num_threads=4,
convergence_threshold=1e-5)
- Using a pre-trained model: It is also possible to use a pre-trained model for record linkage, rather than training a new model from scratch. This can be useful if you have limited data or if you want to reuse a model that has been trained on similar data.
Example:
from splink.models import Model
model = Model.from_serialized_json('path/to/pretrained_model.json')
- Using a different training algorithm: Splink supports several different training algorithms, such as maximum likelihood estimation (MLE) and stochastic gradient descent (SGD). These algorithms can be specified as arguments to the
train()function.
Example:
model_params = train(block_matrix,
training_algorithm='sgd')
- Using a different training framework: Splink can be used with different training frameworks, such as TensorFlow or PyTorch. These frameworks can be specified as arguments to the
train()function.
Example:
model_params = train(block_matrix,
training_framework='tensorflow')
These are some of the main options for training a record linkage model in the “https://github.com/moj-analytical-services/splink_demos/” project. For more information and examples, please refer to the project documentation and tutorials.
