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https://dev.mysql.com/doc/heatwave/en/mys-hwaml-ml-explain-row.html
ML_EXPLAIN_ROW Options You can set the following option in JSON format as needed: prediction_explainer: The name of the prediction explainer that you have trained for this model using ML_EXPLAIN. The ML_EXPLAIN_ROW routine generates explanations ...
https://dev.mysql.com/doc/heatwave/en/mys-hwaml-ml-score.html
The dataset used with ML_SCORE should have the same feature columns as the dataset used to train the model but the data should be different. ML_SCORE scores a model by generating predictions using the feature columns in a labeled dataset as input ...
https://dev.mysql.com/doc/heatwave/en/mys-hwaml-model-quality.html
ML_SCORE scores a model by generating predictions using the feature columns in a labeled dataset as input and comparing the predictions to ground truth values in the target column of the labeled dataset. You cannot score a model with a topic ...
https://dev.mysql.com/doc/heatwave/en/mys-hwaml-model-viewing.html
A negative value can have different interpretations depending on the specific model explainer used for the model. To view the details for the models in your model catalog, query the MODEL_CATALOG table. Before You Begin Review the following: Create ...
https://dev.mysql.com/doc/heatwave/en/mys-hwaml-models-delete.html
Users that create models or have the required privileges to a model on the MODEL_CATALOG table can delete them. Before You Begin Review how to Create a Machine Learning Model. Delete a Model To delete a model from the model catalog table: Query the ...
https://dev.mysql.com/doc/heatwave/en/mys-hwaml-onnx-metadata.html
To learn more about model metadata in the model catalog, see Model Metadata. ONNX Inputs Info Use the data_types_map to map the data type of each column to an ONNX model data type. For example, to convert inputs of the type tensor(float) to ...
https://dev.mysql.com/doc/heatwave/en/mys-hwaml-predictions-ml-predict-row.html
After generating predictions for a row of data, learn how to Generate Explanations for a Row of Data to get insight into which features have the most influence on the predictions. ML_PREDICT_ROW generates predictions for one or more rows of data ...
https://dev.mysql.com/doc/heatwave/en/mys-hwaml-predictions.html
The row or table of data must have the same feature columns as the data used to train the model. Predictions are generated by running ML_PREDICT_ROW or ML_PREDICT_TABLE on trained models. If the target column exists in the data to run predictions ...
https://dev.mysql.com/doc/heatwave/en/mys-hwaml-prepare-a-forecasting-model.html
You have the option to automatically Prepare Training and Testing Datasets with your own data by using the TRAIN_TEST_SPLIT routine. This topic describes how to prepare the data to use for a forecasting machine learning model. To prepare the data ...
https://dev.mysql.com/doc/heatwave/en/mys-hwaml-prepare-an-anomaly-detection-model.html
You have the option to automatically Prepare Training and Testing Datasets with your own data by using the TRAIN_TEST_SPLIT routine. This topic describes how to prepare the data to use for two anomaly detection machine learning models: a ...