ML_PREDICT_ROW
generates predictions for one or more rows of unlabeled data
specified in JSON
format.
ML_PREDICT_ROW
is invoked using a SELECT
statement.
A loaded model is required to run
ML_PREDICT_ROW
.
See Section 3.7.2, “Loading Models”.
SELECT ML_PREDICT_ROW(input_data, model_handle);
ML_PREDICT_ROW
parameters:
-
input_data
: Specifies the data to generate predictions for. A single row of data can be specified explicitly inJSON
format:SELECT sys.ML_PREDICT_ROW(JSON_OBJECT("column_name", value, "column_name", value, ...), model_handle);
ML_PREDICT_ROW
can be run on multiple rows of data by specifying the columns as key-value pairs inJSON
format and selecting from a table:SELECT sys.ML_PREDICT_ROW(JSON_OBJECT("output_col_name", schema.`input_col_name`, output_col_name", schema.`input_col_name`, ...), model_handle) FROM input_table_name LIMIT N;
model_handle
: Specifies the model handle or a session variable containing the model handle.
-
Running
ML_PREDICT_ROW
on a single row of data:SELECT sys.ML_PREDICT_ROW(JSON_OBJECT("sepal length", 7.3, "sepal width", 2.9, "petal length", 6.3, "petal width", 1.8), @iris_model);
-
Running
ML_PREDICT_ROW
on five rows of data selected from an input table:SELECT sys.ML_PREDICT_ROW(JSON_OBJECT("sepal length", iris_test.`sepal length`, "sepal width", iris_test.`sepal width`, "petal length", iris_test.`petal length`, "petal width", iris_test.`petal width`), @iris_model) FROM ml_data.iris_test LIMIT 5;