End-to-end user journeys for ML models
This document describes the user journeys for machine learning (ML) models that are trained in BigQuery ML, including the statements and functions that you can use to work with ML models. BigQuery ML offers the following types of ML models:
-
Supervised learning models:
-
Unsupervised learning models:
-
Transform-only models: Transform-only models aren't typical ML models but are instead artifacts that transform raw data into features.
Model creation user journeys
The following table describes the statements and functions you can use to create and tune models:
1 For a step-by-step example of using hyperparameter tuning, see Improve model performance with hyperparameter tuning .
2
BigQuery ML doesn't offer a function to retrieve the
weights for this model. To see the weights of the model, you can
export the model from BigQuery ML to Cloud Storage and then use the
XGBoost library or the TensorFlow library to visualize the tree
structure for tree models or the graph structure for neural networks. For more
information, see EXPORT MODEL
and Export a BigQuery ML model for online prediction
.
Model use user journeys
The following table describes the statements and functions you can use to evaluate, explain, and get predictions from models:
1
ML.CONFUSION_MATRIX
is only applicable to classification models.
2
ML.ROC_CURVE
is only applicable to binary classification models.
3
The ML.EXPLAIN_PREDICT
function encompasses the ML.PREDICT
function because its output is a superset of the
results of ML.PREDICT
.
4
To understand the difference between ML.GLOBAL_EXPLAIN
and ML.FEATURE_IMPORTANCE
, see the Explainable AI overview
.
5
The ML.ADVANCED_WEIGHTS
function encompasses the ML.WEIGHTS
function because its output is a superset of the
results of ML.WEIGHTS
.
