|
||||||||
Evaluating a Machine Learning Model with BigQuery ML - Python Automation and Machine Learning for ICs - - An Online Book: Python Automation and Machine Learning for ICs by Yougui Liao - |
||||||||
Python Automation and Machine Learning for ICs http://www.globalsino.com/ICs/ | ||||||||
Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix | ||||||||
================================================================================= For models trained within BigQuery ML, the ML.EVALUATE function can be used to assess performance directly, offering a straightforward way to obtain metrics like accuracy, precision, recall, and others appropriate to the model type. Evaluating a machine learning model with BigQuery ML involves several steps, including preparing the data, training the model, and assessing its performance:
BigQuery ML is powerful because it allows you to perform these machine learning tasks directly within your database environment, using SQL-like syntax, which can significantly streamline the process for those already familiar with SQL. Note that BigQuery ML primarily supports evaluating models that have been trained directly within BigQuery using its built-in machine learning capabilities. It provides tools and SQL functions to create, train, evaluate, and predict with several types of machine learning models, such as linear regression, logistic regression, k-means clustering, matrix factorization, time series models, and more. However, BigQuery ML cannot be used to directly evaluate the models developed in TensorFlow, PyTorch, or other external machine learning frameworks. To evaluate such models with BigQuery ML, we would typically need to either:
===========================================
|
||||||||
================================================================================= | ||||||||
|
||||||||