{ "source_file": "0930", "source_platform": "Medium", "author": "Dario Radečić", "title_listed": "How to Train a Decision Tree Classifier... In SQL", "keywords": "SQL,决策树,ML", "domain": "机器学习", "content_type": "深度技术解析", "is_paid_flag": "部分免费/会员墙", "url": "https://towardsdatascience.com/how-to-train-a-decision-tree-classifier-in-sql-e29f37835f18/", "publish_date": "2024-04", "corpus_stage": "预训练/SFT", "notes": "", "id": "d998e1b0a2c3", "collected_at": "2026-10-01T21:13:18", "title": "How to Train a Decision Tree Classifier... In SQL | Towards Data Science", "body_markdown": "![Revisit consent button](https://cdn-cookieyes.com/assets/images/revisit.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. 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In SQL\nSQL can now replace Python for most supervised ML tasks. Should you make the switch?\n[Dario Radečić](https://towardsdatascience.com/author/radecicdario/)\nApril 11, 20248 min read\n![Photo by Resource Database on Unsplash](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/135PA_k1W_x0LboX6q_PciQ-scaled.jpeg)\nPhoto by [Resource Database](https://unsplash.com/@resourcedatabase?utm_content=creditCopyText&utm_medium=referral&utm_source=unsplash) on [Unsplash](https://unsplash.com/photos/a-black-and-white-photo-of-a-circular-object-FNuryNf-8-g?utm_content=creditCopyText&utm_medium=referral&utm_source=unsplash)\nWhen it comes to machine learning, I'm an avid fan of attacking data where it lives. 90%+ of the time, that's going to be a **relational database** , assuming we're talking about supervised machine learning.\nPython is amazing, but pulling dozens of GB of data whenever you want to train a model is a huge **bottleneck** , especially if you need to retrain them frequently. Eliminating data movement makes a lot of sense. SQL is your friend.\nFor this article, I'll use an always-free Oracle Database 21c provisioned on [Oracle Cloud](https://www.oracle.com/cloud/). I'm not sure if you can translate the logic to other database vendors. Oracle works like a charm, and the database you provision won't cost you a dime - ever.\n* * *\nLearn this step by step with the interactive [AI Engineer](https://roadmap.sh/ai-engineer), [AI and Data Scientist](https://roadmap.sh/ai-data-scientist), [Machine Learning](https://roadmap.sh/machine-learning) and [SQL](https://roadmap.sh/sql) roadmaps.\n## Dataset Loading and Preprocessing\nI'll leave the Python vs. Oracle for machine learning on huge dataset comparison for some other time. Today, it's all about getting back to basics.\nI'll use the following dataset today:\n * **Fisher, R.A. (1936)**. The use of multiple measurements in taxonomic problems. University of California, Irvine, School of Information and Computer Sciences. Retrieved from . Licensed under [Creative Commons Attribution 4.0](https://creativecommons.org/licenses/by/4.0/legalcode) license.\n\n\nSo download it to follow along, and make sure you have a connection established to your database instance. Tools like [SQL Developer](https://www.oracle.com/database/sqldeveloper/) or [Visual Studio Code](https://code.visualstudio.com/) can do that for you.\n### How to Create a Database Table\nThe following code snippet will create the `iris` table. The `id` column is mandatory, as Oracle will need it behind the scenes:\nsql\n\n```\ncreate sequence seq_iris;\n\ncreate table iris(\n id number default seq_iris.nextval,\n sepal_length number(2, 1),\n sepal_width number(2, 1),\n petal_length number(2, 1),\n petal_width number(2, 1),\n species varchar2(15)\n);\n```\n\nOnce the table is created, it's time to load the data.\n### How to Load CSV Data Into the Table\nThe source data is in CSV format, so you'll have to use a tool like SQL Developer to load it into a table. Right-click on your table name, select _Import Data_ and provide a path to your CSV file.\nYou'll then need to map the CSV column names to your table column names:\n![Image 1 - Loading a CSV file to a table \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/1F2nouf5nFzXBLi-Mo1Fqw.png)\nImage 1 - Loading a CSV file to a table (image by author)\nOnce done, issue a simple `select` statement to verify the data was loaded successfully:\nsql\n\n```\nselect * from iris;\n```\n\nHere's what you should get back:\n![Image 2 - Iris dataset stored in a database \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/1WHZB8_g5D3hBT7K5GYa7tA.png)\nImage 2 - Iris dataset stored in a database (image by author)\nAll available data is now in one table. It's a great starting point, but not what you want in machine learning.\n### Train/Test Split\nSplitting your data into two subsets makes a lot of sense when building machine learning models. You train the model on one subset (larger) and evaluate it on previously unseen data.\nOracle SQL allows you to **sample** a portion of your data, let's say 75% with a random seed. You can use this technique to create a training set, and then use the `minus` set operator to create a test set:\nsql\n\n```\n-- Train set\ncreate or replace view v_iris_train as\nselect * from iris\nsample(75)\nseed(42);\n\n-- Test set\ncreate or replace view v_iris_test as\nselect * from iris \nminus \nselect * from v_iris_train;\n```\n\nTo see how the data was split, you can print the number of rows from each:\nsql\n\n```\nselect\n (select count(*) from v_iris_train) as n_train_instances,\n (select count(*) from v_iris_test) as n_test_instances\nfrom dual;\n```\n\nNot a perfect 75:25 split, but close enough:\n![Image 3 - Number of training/testing instances \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/1CuOpg5NURyFColL3rCcNQA.png)\nImage 3 - Number of training/testing instances (image by author)\nYou now have everything needed to create a machine learning model in SQL. Let's get right to it!\n## How to Train a Decision Tree Classifier in SQL\n_**Note:** I'm demonstrating how to train a decision tree classifier, but that's not the only model available. You can choose simple linear models, neural networks, and everything in between. Refer to the [official docs](https://docs.oracle.com/en/database/oracle/oracle-database/19/arpls/DBMS_DATA_MINING.html#GUID-409BAB5C-3D02-4495-A68C-F117033C555B) for all available algorithms._\nThere are a couple of things you need to know before training your first ML model in SQL:\n * **You must name your model** - It's an arbitrary string, but you can't create a model if one with a given name already exists. I'll show you how to drop it first.\n * **Each model has its settings** - All are passed into `dbms_data_mining.setting_list`. The ones you're about to see are specific to a decision tree classifier model. Refer to the [official docs](https://docs.oracle.com/en/database/oracle/oracle-database/19/arpls/DBMS_DATA_MINING.html#GUID-03435110-D723-42FD-B4EA-39C86A039566) for more settings, or settings for different models.\n * **Training function** - It's recommended to use the `dbms_data_mining.create_model2()` function to train the model. The `2` in the name is unfortunately named revision of the original function, and it now allows you to pass in model settings directly instead of saving them into a table first.\n\n\nAs for the model, I'll modify the settings to use entropy for the metric and to have a max depth of 5. The model will be named `ml_model_iris`, and will first be dropped if exists.\nThe rest of the code mostly has to do with error handling:\nsql\n\n```\ndeclare\n v_setlist dbms_data_mining.setting_list;\nbegin\n -- 1. Decision tree specific settings\n v_setlist(dbms_data_mining.algo_name) := dbms_data_mining.algo_decision_tree;\n v_setlist(dbms_data_mining.tree_impurity_metric) := dbms_data_mining.tree_impurity_entropy;\n v_setlist(dbms_data_mining.tree_term_max_depth) := '5';\n\n -- 2. Drop the model if it exists\n begin \n dbms_data_mining.drop_model('ml_model_iris');\n exception\n when others then\n -- Model does not exist error code\n if sqlcode = -40284 then null;\n else \n dbms_output.put_line('ERROR training the ML model');\n dbms_output.put_line('Reason: Unable to delete previous ML model.');\n dbms_output.put_line('Message: ' || sqlerrm);\n dbms_output.put_line('--------------------------------------------------');\n end if;\n end;\n\n -- 3. Create the model\n begin\n dbms_data_mining.create_model2(\n model_name => 'ml_model_iris',\n mining_function => 'CLASSIFICATION',\n data_query => 'select * from v_iris_train',\n set_list => v_setlist,\n case_id_column_name => 'id',\n target_column_name => 'species'\n );\n exception\n when others then\n dbms_output.put_line('ERROR training the ML model');\n dbms_output.put_line('Reason: Unable to train the model.');\n dbms_output.put_line('Message: ' || sqlerrm);\n dbms_output.put_line('--------------------------------------------------');\n end;\n\n -- 4. Other potential exceptions\n exception\n when others then\n dbms_output.put_line('ERROR training the ML model');\n dbms_output.put_line('Reason: Other.');\n dbms_output.put_line('Message: ' || sqlerrm);\n dbms_output.put_line('--------------------------------------------------');\nend;\n/\n```\n\nRunning the above PL/SQL snippet will create a bunch of tables. In my opinion, these should be hidden from the table view as they introduce a lot of clutter, especially if you have multiple model versions:\n![Image 4 - Tables created by Oracle \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/1aJFoVWCTmebczM2mOhTMpg.png)\nImage 4 - Tables created by Oracle (image by author)\nBut, the tables carry a lot of useful information. Let's go over it next.\n### How to Extract Model Details\nFor example, the `DM$PX` table shows you the \"decisions made\" by the decision tree model. In other words, it shows which features have to be split where to make the best prediction possible:\n![Image 5 - Model details for decision tree classifier \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/1CWxA8LJIppYOGfakLo0Faw.png)\nImage 5 - Model details for decision tree classifier (image by author)\nOther tables are less relevant, so I'll leave their investigation up to you.\n## How to Make Predictions on the Test Set\nOnto the predictions now. The below code snippet creates a table named `iris_predictions` which contains per-class predictions and prediction probability for every row in the test set.\nThe syntax is somewhat specific to machine learning tasks on the Oracle database, as you need to call the `prediction_set()` function and pass in the model:\nsql\n\n```\ncreate table iris_predictions as (\n select \n s.id, \n t.prediction, \n t.probability\n from v_iris_test s, \n table(prediction_set(ml_model_iris using *)) t\n);\n```\n\nThis will create the table for you, so let's take a peek what's inside:\nsql\n\n```\nselect * from iris_predictions;\n```\n\nThe predictions are per-class based, meaning you'll have as many records for a single instance as there are classes. The Iris dataset has 3 classes, so the `iris_predictions` table will hold 3 * 33 records:\n![Image 6 - Per class prediction probabilities \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/1TeRouzuAjBa7MGgICQTc9A.png)\nImage 6 - Per class prediction probabilities (image by author)\nWith a bit of SQL magic, you can extract only the row in which the `probability` is the highest. The analytical `row_number()` function combined with descending sorting does most of the heavy lifting:\nvbnet\n\n```\nwith ranked_predictions as ( select id, prediction, probability, row_number() over(partition by id order by probability desc) as rn from iris_predictions)select p.id, a.species as actual, p.prediction, p.probability, case when a.species = p.prediction then 1 else 0 end as is_correctfrom ranked_predictions pjoin v_iris_test a on a.id = p.idwhere p.rn = 1;\n```\n\nI've also added an `is_correct` flag, so you can immediately know if the predicted value matches the actual one:\n![Image 7 - Predicted class with is_correct flag \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/1TwalmzM809B3_dUORF8-EA.png)\nImage 7 - Predicted class with is_correct flag (image by author)\nAnd as you can see, that's not always the case:\n![Image 8— Predicted class with is_correct flag \\(2\\) \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/1ZKD-5yIhnBzt1fDIAcTfnQ.png)\nImage 8— Predicted class with is_correct flag (2) (image by author)\nYou now have a prediction result set, but what about model **accuracy on the test set**? What about other metrics, such as the **confusion matrix**? That's what you'll implement next.\n## How to Evaluate Your Decision Tree Classifier Model\nUnfortunately, computing model accuracy on the test set and confusion matrix takes much more code than doing the same in Python.\nYou get the accuracy value for free when calculating the confusion matrix, so that's a plus. The `dbms_data_mining.confusion_matrix()` function requires you to pass in a dozen parameters, all specifying different table names and table column names. The confusion matrix itself will then be saved into a table specified by the`confusion_matrix_table_name` parameter.\nOverall, a lot of work:\nsql\n\n```\ndeclare \n cm_accuracy number;\nbegin\n execute immediate 'drop table iris_confusion_matrix';\n exception\n when others then\n if sqlcode = -942 then null;\n else\n dbms_output.put_line('ERROR making evaluations');\n dbms_output.put_line('Reason: Unable to delete previous table containing confusion matrix.');\n dbms_output.put_line('Message: ' || sqlerrm);\n dbms_output.put_line('--------------------------------------------------');\n end if;\n\n begin\n dbms_data_mining.compute_confusion_matrix(\n accuracy => cm_accuracy,\n apply_result_table_name => 'iris_predictions',\n target_table_name => 'v_iris_test',\n case_id_column_name => 'id',\n target_column_name => 'species',\n confusion_matrix_table_name => 'iris_confusion_matrix',\n score_column_name => 'prediction',\n score_criterion_column_name => 'probability',\n cost_matrix_table_name => null,\n apply_result_schema_name => null,\n target_schema_name => null,\n score_criterion_type => 'PROBABILITY'\n );\n dbms_output.put_line('Accuracy: ' || round(cm_accuracy * 100, 2) || '%');\n exception\n when others then\n dbms_output.put_line('ERROR making evaluations');\n dbms_output.put_line('Reason: Unable to create a confusion matrix table.');\n dbms_output.put_line('Message: ' || sqlerrm);\n dbms_output.put_line('--------------------------------------------------');\n end;\nend;\n/\n```\n\nOnce you run the above PL/SQL block, you'll get the following output:\n![Image 9 - Decision tree classifier accuracy \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/1d3hheK1DLVXosIsNFPZf8g.png)\nImage 9 - Decision tree classifier accuracy (image by author)\nWhich means the model is over 90% accurate on the test set. Nice!\nNow about the confusion matrix:\nsql\n\n```\nselect * from iris_confusion_matrix;\n```\n\nYou can see there are two _Virginica_ instances predicted as _Versicolor_ , and one case going the other way around:\n![Image 10 - Confusion matrix \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/1fKOCBtEbqCqhAsj8yH1PSw.png)\nImage 10 - Confusion matrix (image by author)\nOverall, that's your confusion matrix. It may not be presented in the format you're familiar with, but all the data is there.\n* * *\n## Summing up Machine Learning with SQL\nTo conclude, SQL can now do most things Python can when it comes to supervised machine learning. The important thing to remember is you're trading ease of development for ease of data access, at least if you're coming from a Python background.\nThe current version of Oracle SQL implementation requires more code to do the same, has significantly inferior community support, and could benefit from making the documentation more developer-friendly.\nBut, if transferring data from database to memory is a deal-breaker, it's a completely viable solution for building highly accurate models.\n_What are your thoughts on shifting ML systems to the database? Do you have hands-on experience with a different database vendor?_ Make sure to share in the comment section below.\n**Read next:**\n> [**How to Use OpenAI ChatGPT API... In SQL**](https://towardsdatascience.com/how-to-use-openai-chatgpt-api-in-sql-9b60d2526a9e)\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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