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https://dev.mysql.com/doc/mysql-ai/9.5/en/myai-variables.html
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https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-anomaly-detection-models.html
You can use the following anomaly detection model types: GkNN (Generalized kth Nearest Neighbors) PCA (Principal Component Analysis) GLOF (Generalized Local Outlier Factor) GkNN Model Generalized kth Nearest Neighbors (GkNN) is an algorithm model ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-automl-workflow.html
A typical AutoML workflow is described below: When you run the ML_TRAIN routine, AutoML retrieves the data to use for training. The training data is then distributed across the cluster, which performs machine learning computation in parallel.
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-classification-generate-explanation.html
After training a classification model, you can query the default model explanation or query new model explanations. Explanations help you understand which features had the most influence on generating predictions. A positive value indicates that a ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-classification-generate-prediction.html
To generate predictions, use the sample data from the testing_data dataset. Even though the table has labels for the Approved target column, the column is not considered when generating predictions. This allows you to compare the predictions to the ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-classification-overview.html
This topic describes how to prepare the data to use for a classification machine learning model. The classification use-case is to approve or reject loan applications for clients based on their personal and socioeconomic status, assets, ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-classification-score.html
After generating predictions and explanations, you can score the model to assess its reliability. For a list of scoring metrics you can use with classification models, see Classification Metrics. For this use case, you use the test dataset for ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-classification-train.html
After preparing the data for a classification model, you can train the model. Before You Begin Review and complete all the tasks to Prepare Data for a Classification Model. Training the Model Train the model with the ML_TRAIN routine and use the ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-explainers.html
After the ML_TRAIN routine, use the ML_EXPLAIN routine to train model explainers for AutoML. By default, the ML_TRAIN routine trains the Permutation Importance model explainer. Feature importance is presented as a value ranging from -1 to 1. A ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-explanations-ml-explain-row.html
ML_EXPLAIN_ROW explains predictions for one or more rows of unlabeled data. The following example trains a dataset with the classification machine learning task. mysql> CALL sys.ML_TRAIN('census_data.census_train', 'revenue', JSON_OBJECT('task', ...