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Panoramica della regressione
Un caso d'uso comune del machine learning è la previsione del valore di una metrica numerica per i nuovi dati utilizzando un modello addestrato su dati storici simili.
Ad esempio, potresti voler prevedere il prezzo di vendita previsto di una casa. Utilizzando la posizione e le caratteristiche della casa come elementi, puoi confrontarla con case simili già vendute e utilizzare i relativi prezzi di vendita per stimare il prezzo di vendita della casa.
Puoi utilizzare uno dei seguenti modelli in combinazione con la
funzione ML.PREDICT
per eseguire la regressione:
Conoscenze consigliate
Utilizzando le impostazioni predefinite nelle istruzioni CREATE MODEL e nella funzione
ML.PREDICT, puoi creare e utilizzare un modello di regressione anche
senza molte conoscenze di ML. Tuttavia, avere conoscenze di base sullo sviluppo del ML ti aiuta a ottimizzare sia i dati sia il modello per ottenere risultati migliori. Ti consigliamo di utilizzare le seguenti risorse per acquisire familiarità con le tecniche e le procedure di ML:
Salvo quando diversamente specificato, i contenuti di questa pagina sono concessi in base alla licenza Creative Commons Attribution 4.0, mentre gli esempi di codice sono concessi in base alla licenza Apache 2.0. Per ulteriori dettagli, consulta le norme del sito di Google Developers. Java è un marchio registrato di Oracle e/o delle sue consociate.
Ultimo aggiornamento 2025-08-17 UTC.
[[["Facile da capire","easyToUnderstand","thumb-up"],["Il problema è stato risolto","solvedMyProblem","thumb-up"],["Altra","otherUp","thumb-up"]],[["Mancano le informazioni di cui ho bisogno","missingTheInformationINeed","thumb-down"],["Troppo complicato/troppi passaggi","tooComplicatedTooManySteps","thumb-down"],["Obsoleti","outOfDate","thumb-down"],["Problema di traduzione","translationIssue","thumb-down"],["Problema relativo a esempi/codice","samplesCodeIssue","thumb-down"],["Altra","otherDown","thumb-down"]],["Ultimo aggiornamento 2025-08-17 UTC."],[[["\u003cp\u003eRegression models are used to predict numerical values for new data based on patterns learned from historical data, such as predicting a house's sale price.\u003c/p\u003e\n"],["\u003cp\u003eThe \u003ccode\u003eML.PREDICT\u003c/code\u003e function can be used in conjunction with various models, including linear regression, boosted tree, random forest, deep neural network (DNN), wide & deep, and AutoML models, to perform regression.\u003c/p\u003e\n"],["\u003cp\u003eYou can create and use a regression model with default settings without extensive machine learning (ML) knowledge, though basic ML familiarity can help improve results.\u003c/p\u003e\n"],["\u003cp\u003eSeveral resources like Google's Machine Learning Crash Course and Kaggle's ML tutorials are available to help build familiarity with ML techniques and processes.\u003c/p\u003e\n"]]],[],null,["# Regression overview\n===================\n\nA common use case for machine learning is predicting the value of a numerical\nmetric for new data by using a model trained on similar historical data.\nFor example, you might want to predict a house's expected sale price. By using\nthe house's location and characteristics as features, you can compare this house\nto similar houses that have already sold, and use their sales prices to estimate\nthe house's sale price.\n\nYou can use any of the following models in combination with the\n[`ML.PREDICT` function](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-predict)\nto perform regression:\n\n- [Linear regression models](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-glm): use [linear regression](https://developers.google.com/machine-learning/crash-course/linear-regression) by setting the `MODEL_TYPE` option to `LINEAR_REG`.\n- [Boosted tree models](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-boosted-tree): use a [gradient boosted decision tree](https://developers.google.com/machine-learning/decision-forests/intro-to-gbdt) by setting the `MODEL_TYPE` option to `BOOSTED_TREE_REGRESSOR`.\n- [Random forest models](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-random-forest): use a [random forest](https://developers.google.com/machine-learning/decision-forests/intro-to-decision-forests) by setting the `MODEL_TYPE` option to `RANDOM_FOREST_REGRESSOR`.\n- [Deep neural network (DNN) models](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-dnn-models): use a [neural network](https://developers.google.com/machine-learning/crash-course/neural-networks) by setting the `MODEL_TYPE` option to `DNN_REGRESSOR`.\n- [Wide \\& Deep models](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-wnd-models): use [wide \\& deep learning](https://dl.acm.org/doi/10.1145/2988450.2988454) by setting the `MODEL_TYPE` option to `DNN_LINEAR_COMBINED_REGRESSOR`.\n- [AutoML models](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-automl): use an [AutoML classification model](/vertex-ai/docs/tabular-data/classification-regression/overview) by setting the `MODEL_TYPE` option to `AUTOML_REGRESSOR`.\n\nRecommended knowledge\n---------------------\n\nBy using the default settings in the `CREATE MODEL` statements and the\n`ML.PREDICT` function, you can create and use a regression model even\nwithout much ML knowledge. However, having basic knowledge about\nML development helps you optimize both your data and your model to\ndeliver better results. We recommend using the following resources to develop\nfamiliarity with ML techniques and processes:\n\n- [Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course)\n- [Intro to Machine Learning](https://www.kaggle.com/learn/intro-to-machine-learning)\n- [Intermediate Machine Learning](https://www.kaggle.com/learn/intermediate-machine-learning)"]]
