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OML4SQL Classification SVM.dsnb
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[{"layout":null,"template":null,"templateConfig":null,"name":"OML4SQL Classification SVM","description":null,"readOnly":false,"type":"medium","paragraphs":[{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":null,"title":null,"message":[],"enabled":true,"result":{"startTime":1715312793797,"interpreter":"md.medium","endTime":1715312793861,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Classification Modeling to Predict Target Customers using Support Vector Machine","","In this notebook, we predict customers most likely to be positive responders to an Affinity Card loyalty program. High Affinity Card responders (target value = 1) are defined as those customers who when given a loyaly or affinity card hyper-respond i.e. they increae their purchasing higher than the Affinity Card program's offered discount percentage. This notebook builds and applies classification support vector machine models using the SH schema data. All processing occurs inside Oracle Autonomous Database.","","Copyright (c) 2024 Oracle Corporation ","###### <a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a>","---"],"enabled":true,"result":{"startTime":1715312793938,"interpreter":"md.medium","endTime":1715312794172,"results":[{"message":"<h2 id=\"classification-modeling-to-predict-target-customers-using-support-vector-machine\">Classification Modeling to Predict Target Customers using Support Vector Machine<\/h2>\n<p>In this notebook, we predict customers most likely to be positive responders to an Affinity Card loyalty program. High Affinity Card responders (target value = 1) are defined as those customers who when given a loyaly or affinity card hyper-respond i.e. they increae their purchasing higher than the Affinity Card program's offered discount percentage. This notebook builds and applies classification support vector machine models using the SH schema data. All processing occurs inside Oracle Autonomous Database.<\/p>\n<p>Copyright (c) 2024 Oracle Corporation<\/p>\n<h6 id=\"the-universal-permissive-license-upl-version-10\"><a href=\"https://oss.oracle.com/licenses/upl/\" onclick=\"return ! window.open('https://oss.oracle.com/licenses/upl/');\">The Universal Permissive License (UPL), Version 1.0<\/a><\/h6>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":9,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","<dl>","<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>","<\/dl>"],"enabled":true,"result":{"startTime":1715312794252,"interpreter":"md.medium","endTime":1715312794314,"results":[{"message":"<dl>\n<img src=\"http://www.oracle.com/technetwork/database/options/advanced-analytics/classification-5663162.jpg\" alt=\"OML Notebooks\" width=\"250\"/>\n<\/dl>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":3,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":"For more information...","message":["%md","","* <a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a>","* <a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a>","* <a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a>","* <a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a>","* <a href=\"https://oracle.com/goto/ml-support-vector-machine\" target=\"_blank\">OML Support Vector Machine<\/a>"],"enabled":true,"result":{"startTime":1715312794393,"interpreter":"md.medium","endTime":1715312794455,"results":[{"message":"<ul>\n<li><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/index.html\" target=\"_blank\">Oracle ADB Documentation<\/a><\/li>\n<li><a href=\"https://github.com/oracle-samples/oracle-db-examples/tree/main/machine-learning\" target=\"_blank\">OML folder on Oracle GitHub<\/a><\/li>\n<li><a href=\"https://www.oracle.com/machine-learning\" target=\"_blank\">OML Web Page<\/a><\/li>\n<li><a href=\"https://www.oracle.com/goto/ml-classification\" target=\"_blank\">OML Classification<\/a><\/li>\n<li><a href=\"https://oracle.com/goto/ml-support-vector-machine\" target=\"_blank\">OML Support Vector Machine<\/a><\/li>\n<\/ul>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display the SUPPLEMENTARY_DEMOGRAPHICS data ","message":["%sql","","SELECT * ","FROM SH.SUPPLEMENTARY_DEMOGRAPHICS","FETCH FIRST 10 ROWS 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Bach.\tOther\t1\t1\t0\t1\t1\t0\t1\t1\t1\t0\t\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Count number of records in SUPPLEMENTARY_DEMOGRAPHICS table","message":["%sql","","SELECT COUNT(*) FROM SH.SUPPLEMENTARY_DEMOGRAPHICS;"],"enabled":true,"result":{"startTime":1715312794770,"interpreter":"sql.medium","endTime":1715312795042,"results":[{"message":"COUNT(*)\n4500\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":4,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"bar\":{\"showSeries\":[\"COUNT\"],\"aggregationOption\":\"Sum\",\"series\":{\"selectedId\":\"COUNT\",\"availableSeriesElements\":[{\"id\":\"COUNT\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"axis\":{\"x\":{\"title\":\"Response (1 = Responded, 0 = No Response)\"}},\"lastColumns\":[\"AFFINITY_CARD\",\"COUNT\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Show distribution of AFFINITY_CARD responders","message":["%sql","","SELECT AFFINITY_CARD, COUNT(*) COUNT ","FROM SH.SUPPLEMENTARY_DEMOGRAPHICS","GROUP BY AFFINITY_CARD;"],"enabled":true,"result":{"startTime":1715312795135,"interpreter":"sql.medium","endTime":1715312795257,"results":[{"message":"AFFINITY_CARD\tCOUNT\n0\t3428\n1\t1072\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":8,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"bar\":{\"groupByColumns\":[\"AFFINITY_CARD\",\"HOUSEHOLD_SIZE\"],\"series\":{\"availableSeriesElements\":[{\"id\":\"NUM_CUSTOMERS\",\"lineType\":\"straight\",\"borderColor\":\"rgb(50, 146, 94)\",\"borderWidth\":0,\"color\":\"rgb(50, 146, 94)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(50, 146, 94)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"NUM_CUSTOMERS\",\"HOUSEHOLD_SIZE\",\"AFFINITY_CARD\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Graph HOUSEHOLD_SIZE grouped by AFFINITY_CARD responders","message":["%sql","","SELECT COUNT(CUST_ID) AS NUM_CUSTOMERS, HOUSEHOLD_SIZE, AFFINITY_CARD ","FROM SH.SUPPLEMENTARY_DEMOGRAPHICS ","GROUP BY HOUSEHOLD_SIZE, AFFINITY_CARD;"],"enabled":true,"result":{"startTime":1715312795336,"interpreter":"sql.medium","endTime":1715312795457,"results":[{"message":"NUM_CUSTOMERS\tHOUSEHOLD_SIZE\tAFFINITY_CARD\n476\t9+\t0\n814\t3\t1\n107\t4-5\t1\n681\t1\t0\n1040\t2\t0\n29\t9+\t1\n112\t4-5\t0\n146\t6-8\t0\n2\t6-8\t1\n109\t2\t1\n11\t1\t1\n973\t3\t0\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"bar\":{\"showSeries\":[\"AFFINITY_0_COUNT\",\"AFFINITY_1_COUNT\"],\"aggregationOption\":\"Last\",\"series\":{\"availableSeriesElements\":[{\"id\":\"AFFINITY_0_COUNT\",\"lineType\":\"straight\",\"borderColor\":\"rgb(50, 146, 94)\",\"borderWidth\":0,\"color\":\"rgb(50, 146, 94)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(50, 146, 94)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0},{\"id\":\"AFFINITY_1_COUNT\",\"lineType\":\"straight\",\"borderColor\":\"rgb(50, 146, 94)\",\"borderWidth\":0,\"color\":\"rgb(50, 146, 94)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(50, 146, 94)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"HOUSEHOLD_SIZE\",\"AFFINITY_1_COUNT\",\"AFFINITY_0_COUNT\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"bar","title":"Graph HOUSEHOLD_SIZE grouped by AFFINITY_CARD responders","message":["%sql","","SELECT HOUSEHOLD_SIZE,"," SUM(CASE WHEN AFFINITY_CARD = 1 THEN NUM_CUSTOMERS ELSE 0 END) AS AFFINITY_1_COUNT,"," SUM(CASE WHEN AFFINITY_CARD = 0 THEN NUM_CUSTOMERS ELSE 0 END) AS AFFINITY_0_COUNT","FROM (SELECT COUNT(CUST_ID) AS NUM_CUSTOMERS, HOUSEHOLD_SIZE, AFFINITY_CARD "," FROM SH.SUPPLEMENTARY_DEMOGRAPHICS "," GROUP BY HOUSEHOLD_SIZE, AFFINITY_CARD)","GROUP BY HOUSEHOLD_SIZE","ORDER BY HOUSEHOLD_SIZE"],"enabled":true,"result":{"startTime":1715312795545,"interpreter":"sql.medium","endTime":1715312795668,"results":[{"message":"HOUSEHOLD_SIZE\tAFFINITY_1_COUNT\tAFFINITY_0_COUNT\n1\t11\t681\n2\t109\t1040\n3\t814\t973\n4-5\t107\t112\n6-8\t2\t146\n9+\t29\t476\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create view DEMOGRAPHICS4_V with desired columns for analysis","message":["%script","","CREATE OR REPLACE VIEW DEMOGRAPHICS4_V "," AS SELECT AFFINITY_CARD, BOOKKEEPING_APPLICATION,"," BULK_PACK_DISKETTES, CUST_ID, EDUCATION,"," FLAT_PANEL_MONITOR, HOME_THEATER_PACKAGE, "," HOUSEHOLD_SIZE, OCCUPATION, OS_DOC_SET_KANJI,"," PRINTER_SUPPLIES, YRS_RESIDENCE, Y_BOX_GAMES"," FROM SH.SUPPLEMENTARY_DEMOGRAPHICS;"," "],"enabled":true,"result":{"startTime":1715312795746,"interpreter":"script.medium","endTime":1715312796332,"results":[{"message":"\nView DEMOGRAPHICS4_V created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Display data from DEMOGRAPHICS4_V view","message":["%sql ","","SELECT * ","FROM DEMOGRAPHICS4_V","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1715312796416,"interpreter":"sql.medium","endTime":1715312796534,"results":[{"message":"AFFINITY_CARD\tBOOKKEEPING_APPLICATION\tBULK_PACK_DISKETTES\tCUST_ID\tEDUCATION\tFLAT_PANEL_MONITOR\tHOME_THEATER_PACKAGE\tHOUSEHOLD_SIZE\tOCCUPATION\tOS_DOC_SET_KANJI\tPRINTER_SUPPLIES\tYRS_RESIDENCE\tY_BOX_GAMES\n0\t0\t1\t102547\t10th\t1\t0\t1\tOther\t0\t1\t0\t1\n0\t0\t1\t101050\t10th\t1\t0\t1\tOther\t0\t1\t0\t1\n0\t0\t1\t100040\t11th\t1\t0\t1\tSales\t0\t1\t0\t1\n0\t1\t0\t102117\tHS-grad\t0\t0\t1\tFarming\t0\t1\t0\t1\n0\t0\t1\t101074\t10th\t1\t0\t1\tHandler\t0\t1\t1\t1\n0\t0\t1\t104179\t10th\t1\t0\t1\tHandler\t0\t1\t1\t1\n0\t0\t0\t100417\t11th\t0\t0\t1\tHandler\t0\t1\t1\t1\n0\t1\t1\t101146\t< Bach.\t1\t0\t1\t?\t0\t1\t1\t1\n0\t1\t1\t103420\t< Bach.\t1\t0\t1\t?\t0\t1\t1\t1\n0\t1\t1\t101987\t< Bach.\t1\t0\t1\tOther\t0\t1\t1\t1\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Create train and test data sets (60/40) for model build and test","message":["%script","","CREATE OR REPLACE VIEW TRAIN_DATA_CLAS AS SELECT * FROM DEMOGRAPHICS4_V SAMPLE (60) SEED (1);","CREATE OR REPLACE VIEW TEST_DATA_CLAS AS SELECT * FROM DEMOGRAPHICS4_V MINUS SELECT * FROM TRAIN_DATA_CLAS;"],"enabled":true,"result":{"startTime":1715312796612,"interpreter":"script.medium","endTime":1715312796797,"results":[{"message":"\nView TRAIN_DATA_CLAS created.\n\n\n---------------------------\n\nView TEST_DATA_CLAS created.\n\n\n---------------------------\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build a SVM Linear classification model for predicting AFFINITY_CARD","---"],"enabled":true,"result":{"startTime":1715312796875,"interpreter":"md.medium","endTime":1715312796936,"results":[{"message":"<h3 id=\"build-a-svm-linear-classification-model-for-predicting-affinity_card\">Build a SVM Linear classification model for predicting AFFINITY_CARD<\/h3>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a SVM model using default settings","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('SVM_CLASS_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE"," v_setlst DBMS_DATA_MINING.SETTING_LIST;"," ","BEGIN"," v_setlst('PREP_AUTO') := 'ON';"," v_setlst('ALGO_NAME') := 'ALGO_SUPPORT_VECTOR_MACHINES';"," "," DBMS_DATA_MINING.CREATE_MODEL2("," MODEL_NAME => 'SVM_CLASS_MODEL',"," MINING_FUNCTION => 'CLASSIFICATION',"," DATA_QUERY => 'SELECT * FROM TRAIN_DATA_CLAS',"," SET_LIST => v_setlst,"," CASE_ID_COLUMN_NAME => 'CUST_ID',"," TARGET_COLUMN_NAME => 'AFFINITY_CARD');","END;"],"enabled":true,"result":{"startTime":1715312797016,"interpreter":"script.medium","endTime":1715312800616,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"html\":{\"height\":400,\"lastColumns\":[]}}]","hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Examples of possible setting overrides for SVM ","","If the user does not override the default settings, then relevant settings are determined by the algorithm.","","A complete list of settings can be found in the Documentation link:","- Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB');\">Support Vector Machine<\/a>","","- Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75');\">All algorithms<\/a>","- Specify a row weight column ","> v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '<row_weight_column_name>';"," ","* Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is `ODMS_MISSING_VALUE_AUTO`. The option `ODMS_MISSING_VALUE_MEAN_MODE` replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option `ODMS_MISSING_VALUE_AUTO` performs different strategies for different algorithms. When `ODMS_MISSING_VALUE_TREATMENT` is set to `ODMS_MISSING_VALUE_DELETE_ROW`, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.","> v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';"," ","- Switch between Kernel Types - Linear or Gaussian. By default the system uses the Linear Kernel. The linear kernel also is able to return the coefficients, while this is not available on the Gaussian.","> v_setlst('SVMS_KERNEL_FUNCTION') := 'SVMS_LINEAR';","","> v_setlst('SVMS_KERNEL_FUNCTION') := 'SVMS_GAUSSIAN';"," ","- Value of complexity factor for SVM algorithm (both classification and regression). Default value estimated from the data by the algorithm.","> v_setlst('SVMS_COMPLEXITY_FACTOR') := '0.1';"," ","- Type of regularization that the SGD SVM solver uses (only available for linear SVM models). The default is system determined because it depends on the potential model size. Also the Regularization setting for regression (Epsilon specifies the allowable residuals, or noise, in the data). ","> v_setlst('SVMS_REGULARIZER') := 'SVMS_REGULARIZER_L1';","","> v_setlst('SVMS_REGULARIZER') := 'SVMS_REGULARIZER_L2';","","> v_setlst('SVMS_EPSILON') := '0.1';"," ","- Batch rows sets the size of the batch for the SGD solver (linear kernel only). An input of 0 triggers a data driven batch size estimate. The default is 20,000. ","> v_setlst('SVMS_BATCH_ROWS') := '20000';"," ","- Solver type, which cannot be selected if the kernel is non-linear. The default value is system determined. ","> v_setlst('SVMS_SOLVER') := 'SVMS_SOLVER_SGD';","","> v_setlst('SVMS_SOLVER') := 'SVMS_SOLVER_IPM';"],"enabled":true,"result":{"startTime":1715312800699,"interpreter":"md.medium","endTime":1715312800785,"results":[{"message":"<h3 id=\"examples-of-possible-setting-overrides-for-svm\">Examples of possible setting overrides for SVM<\/h3>\n<p>If the user does not override the default settings, then relevant settings are determined by the algorithm.<\/p>\n<p>A complete list of settings can be found in the Documentation link:<\/p>\n<ul>\n<li>\n<p>Algorithm Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-12408982-E738-4D0F-A2BC-84D895E07ABB');\">Support Vector Machine<\/a><\/p>\n<\/li>\n<li>\n<p>Shared Settings: <a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75\" onclick=\"return ! window.open('https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/DBMS_DATA_MINING.html#GUID-24047A09-0542-4870-91D8-329F28B0ED75');\">All algorithms<\/a><\/p>\n<\/li>\n<li>\n<p>Specify a row weight column<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_ROW_WEIGHT_COLUMN_NAME') := '<row_weight_column_name>';<\/p>\n<\/blockquote>\n<ul>\n<li>Specify a missing value treatment method for the training data. This setting does not affect the scoring data. The default value is <code>ODMS_MISSING_VALUE_AUTO<\/code>. The option <code>ODMS_MISSING_VALUE_MEAN_MODE<\/code> replaces missing values with the mean (numeric attributes) or the mode (categorical attributes) both at build time and apply time where appropriate. The option <code>ODMS_MISSING_VALUE_AUTO<\/code> performs different strategies for different algorithms. When <code>ODMS_MISSING_VALUE_TREATMENT<\/code> is set to <code>ODMS_MISSING_VALUE_DELETE_ROW<\/code>, the rows in the training data that contain missing values are deleted. However, if you want to replicate this missing value treatment in the scoring data, then you must perform the transformation explicitly.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('ODMS_MISSING_VALUE_TREATMENT') := 'ODMS_MISSING_VALUE_AUTO';<\/p>\n<\/blockquote>\n<ul>\n<li>Switch between Kernel Types - Linear or Gaussian. By default the system uses the Linear Kernel. The linear kernel also is able to return the coefficients, while this is not available on the Gaussian.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVMS_KERNEL_FUNCTION') := 'SVMS_LINEAR';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('SVMS_KERNEL_FUNCTION') := 'SVMS_GAUSSIAN';<\/p>\n<\/blockquote>\n<ul>\n<li>Value of complexity factor for SVM algorithm (both classification and regression). Default value estimated from the data by the algorithm.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVMS_COMPLEXITY_FACTOR') := '0.1';<\/p>\n<\/blockquote>\n<ul>\n<li>Type of regularization that the SGD SVM solver uses (only available for linear SVM models). The default is system determined because it depends on the potential model size. Also the Regularization setting for regression (Epsilon specifies the allowable residuals, or noise, in the data).<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVMS_REGULARIZER') := 'SVMS_REGULARIZER_L1';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('SVMS_REGULARIZER') := 'SVMS_REGULARIZER_L2';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('SVMS_EPSILON') := '0.1';<\/p>\n<\/blockquote>\n<ul>\n<li>Batch rows sets the size of the batch for the SGD solver (linear kernel only). An input of 0 triggers a data driven batch size estimate. The default is 20,000.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVMS_BATCH_ROWS') := '20000';<\/p>\n<\/blockquote>\n<ul>\n<li>Solver type, which cannot be selected if the kernel is non-linear. The default value is system determined.<\/li>\n<\/ul>\n<blockquote>\n<p>v_setlst('SVMS_SOLVER') := 'SVMS_SOLVER_SGD';<\/p>\n<\/blockquote>\n<blockquote>\n<p>v_setlst('SVMS_SOLVER') := 'SVMS_SOLVER_IPM';<\/p>\n<\/blockquote>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a SVM Model using linear kernel with options","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('SVM_CLASS_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE"," v_setlst DBMS_DATA_MINING.SETTING_LIST;"," ","BEGIN"," v_setlst('PREP_AUTO') := 'ON';"," v_setlst('ALGO_NAME') := 'ALGO_SUPPORT_VECTOR_MACHINES';"," v_setlst('SVMS_KERNEL_FUNCTION') := 'SVMS_LINEAR';"," v_setlst('SVMS_SOLVER') := 'SVMS_SOLVER_SGD';"," v_setlst('SVMS_CONV_TOLERANCE') := '0.001'; "," v_setlst('SVMS_BATCH_ROWS') := '2000';"," v_setlst('SVMS_REGULARIZER') := 'SVMS_REGULARIZER_L1';"," "," DBMS_DATA_MINING.CREATE_MODEL2("," MODEL_NAME => 'SVM_CLASS_MODEL',"," MINING_FUNCTION => 'CLASSIFICATION',"," DATA_QUERY => 'SELECT * FROM TRAIN_DATA_CLAS',"," SET_LIST => v_setlst,"," CASE_ID_COLUMN_NAME => 'CUST_ID',"," TARGET_COLUMN_NAME => 'AFFINITY_CARD');","END;"],"enabled":true,"result":{"startTime":1715312800865,"interpreter":"script.medium","endTime":1715312803164,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","### Build a SVM Gaussian classification model for predicting AFFINITY_CARD","---"],"enabled":true,"result":{"startTime":1715312803246,"interpreter":"md.medium","endTime":1715312803316,"results":[{"message":"<h3 id=\"build-a-svm-gaussian-classification-model-for-predicting-affinity_card\">Build a SVM Gaussian classification model for predicting AFFINITY_CARD<\/h3>\n<hr />\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Build a SVM model using Gaussian kernel with options","message":["%script","","BEGIN DBMS_DATA_MINING.DROP_MODEL('SVM_CLASS_MODEL');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","DECLARE"," v_setlst DBMS_DATA_MINING.SETTING_LIST;","BEGIN"," v_setlst('PREP_AUTO') := 'ON';"," v_setlst('ALGO_NAME') := 'ALGO_SUPPORT_VECTOR_MACHINES';"," v_setlst('SVMS_KERNEL_FUNCTION') := 'SVMS_GAUSSIAN'; "," v_setlst('SVMS_CONV_TOLERANCE') := '0.001'; "," v_setlst('SVMS_NUM_PIVOTS') := '200';"," "," DBMS_DATA_MINING.CREATE_MODEL2("," MODEL_NAME => 'SVM_CLASS_MODEL',"," MINING_FUNCTION => 'CLASSIFICATION',"," DATA_QUERY => 'SELECT * FROM TRAIN_DATA_CLAS',"," SET_LIST => v_setlst,"," CASE_ID_COLUMN_NAME => 'CUST_ID',"," TARGET_COLUMN_NAME => 'AFFINITY_CARD');","END;"],"enabled":true,"result":{"startTime":1715312803391,"interpreter":"script.medium","endTime":1715312807289,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get list of model views","message":["%sql ","","SELECT VIEW_NAME, VIEW_TYPE "," FROM USER_MINING_MODEL_VIEWS"," WHERE MODEL_NAME='SVM_CLASS_MODEL'"," ORDER BY VIEW_NAME;"," "," "," "," "],"enabled":true,"result":{"startTime":1715312807372,"interpreter":"sql.medium","endTime":1715312807496,"results":[{"message":"VIEW_NAME\tVIEW_TYPE\nDM$VCSVM_CLASS_MODEL\tScoring Cost Matrix\nDM$VGSVM_CLASS_MODEL\tGlobal Name-Value Pairs\nDM$VNSVM_CLASS_MODEL\tNormalization and Missing Value Handling\nDM$VSSVM_CLASS_MODEL\tComputed Settings\nDM$VTSVM_CLASS_MODEL\tClassification Targets\nDM$VWSVM_CLASS_MODEL\tModel Build Alerts\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get coefficients of SVM model","message":["%sql","","SELECT * from DM$VNSVM_CLASS_MODEL;"],"enabled":true,"result":{"startTime":1715312807578,"interpreter":"sql.medium","endTime":1715312807706,"results":[{"message":"PARTITION_NAME\tATTRIBUTE_NAME\tATTRIBUTE_SUBNAME\tNUMERIC_MISSING_VALUE\tCATEGORICAL_MISSING_VALUE\tNORMALIZATION_SHIFT\tNORMALIZATION_SCALE\n\tEDUCATION\t\t\tHS-grad\t\t\n\tHOUSEHOLD_SIZE\t\t\t3\t\t\n\tOCCUPATION\t\t\tCrafts\t\t\n\tBOOKKEEPING_APPLICATION\t\t0.8783128032848078\t\t0.8783128032848078\t0.3269852183756386\n\tBULK_PACK_DISKETTES\t\t0.6360582306830922\t\t0.6360582306830922\t0.48122198469071364\n\tFLAT_PANEL_MONITOR\t\t0.5759611795446065\t\t0.5759611795446065\t0.49428847631749057\n\tHOME_THEATER_PACKAGE\t\t0.5591638671145956\t\t0.5591638671145956\t0.49657998650332785\n\tOS_DOC_SET_KANJI\t\t0.0029861888764464357\t\t0.0029861888764464357\t0.054574566460277744\n\tPRINTER_SUPPLIES\t\t1.0\t\t1.0\t1.0\n\tYRS_RESIDENCE\t\t4.001493094438232\t\t4.001493094438232\t1.9178414346193704\n\tY_BOX_GAMES\t\t0.3154162000746549\t\t0.3154162000746549\t0.4647681695563441\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get computed settings","message":["%sql","","SELECT * from DM$VSSVM_CLASS_MODEL;"],"enabled":true,"result":{"startTime":1715312807789,"interpreter":"sql.medium","endTime":1715312807894,"results":[{"message":"PARTITION_NAME\tSETTING_NAME\tSETTING_VALUE\n\tSVMS_SOLVER\tSVMS_SOLVER_IPM\n\tSVMS_STD_DEV\t2.3452078799117149\n\tSVMS_NUM_ITERATIONS\t30\n\tSVMS_COMPLEXITY_FACTOR\t10\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get global settings","message":["%sql","","SELECT * from DM$VGSVM_CLASS_MODEL;"],"enabled":true,"result":{"startTime":1715312807976,"interpreter":"sql.medium","endTime":1715312808078,"results":[{"message":"PARTITION_NAME\tNAME\tNUMERIC_VALUE\tSTRING_VALUE\n\tNUM_ROWS\t2679\t\n\tCONVERGED\t\tYES\n\tITERATIONS\t16\t\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":6,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"raw","title":"Evaluate the model","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE APPLY_RESULT PURGE';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","BEGIN EXECUTE IMMEDIATE 'DROP TABLE LIFT_TABLE PURGE';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","BEGIN"," DBMS_DATA_MINING.APPLY('SVM_CLASS_MODEL','TEST_DATA_CLAS','CUST_ID','APPLY_RESULT');"," DBMS_DATA_MINING.COMPUTE_LIFT('APPLY_RESULT','TEST_DATA_CLAS','CUST_ID','AFFINITY_CARD',"," 'LIFT_TABLE','1','PREDICTION','PROBABILITY',100);","END;"],"enabled":true,"result":{"startTime":1715312808160,"interpreter":"script.medium","endTime":1715312812259,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"table\":{\"version\":1},\"area\":{\"showSeries\":[\"GAIN_CUMULATIVE\"],\"series\":{\"availableSeriesElements\":[{\"id\":\"GAIN_CUMULATIVE\",\"lineType\":\"straight\",\"borderColor\":\"rgb(50, 146, 94)\",\"borderWidth\":0,\"color\":\"rgb(50, 146, 94)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(50, 146, 94)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"QUANTILE_NUMBER\",\"GAIN_CUMULATIVE\"],\"version\":1},\"line\":{\"showSeries\":[\"GAIN_CUMULATIVE\"],\"series\":{\"availableSeriesElements\":[{\"id\":\"GAIN_CUMULATIVE\",\"lineType\":\"straight\",\"borderColor\":\"rgb(25, 95, 116)\",\"borderWidth\":0,\"color\":\"rgb(25, 95, 116)\",\"pattern\":\"auto\",\"markerColor\":\"rgb(25, 95, 116)\",\"markerDisplayed\":\"auto\",\"markerShape\":\"auto\",\"markerSize\":0}]},\"lastColumns\":[\"QUANTILE_NUMBER\",\"GAIN_CUMULATIVE\"],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"line","title":"View model's cumulative gains (lift) chart","message":["%sql","","-- Create a Line chart of QUANTILE_NUMBER (Keys), GAIN_CUMULATIVE(SUM) (Values); No Groups","-- Hint: Try changing this to Bar chart from the below visual settings","","SELECT QUANTILE_NUMBER, GAIN_CUMULATIVE FROM LIFT_TABLE;"],"enabled":true,"result":{"startTime":1715312812339,"interpreter":"sql.medium","endTime":1715312812468,"results":[{"message":"QUANTILE_NUMBER\tGAIN_CUMULATIVE\n1\t3.4562211981566820276497695852534562212E-02\n2\t6.68202764976958525345622119815668202765E-02\n3\t9.71033562163603479262672811059907834101E-02\n4\t1.27496156824349258064516129032258064516E-01\n5\t1.53225806451612903225806451612903225807E-01\n6\t1.88940092165898617511520737327188940092E-01\n7\t2.21198156682027649769585253456221198157E-01\n8\t2.58064516129032258064516129032258064516E-01\n9\t2.90322580645161290322580645161290322581E-01\n10\t3.20276497695852534562211981566820276498E-01\n11\t3.41013824884792626728110599078341013825E-01\n12\t3.73271889400921658986175115207373271889E-01\n13\t4.07834101382488479262672811059907834101E-01\n14\t4.44700460829493087557603686635944700461E-01\n15\t4.67741935483870967741935483870967741936E-01\n16\t4.91935483870967741935483870967741935484E-01\n17\t5.23041474654377880184331797235023041475E-01\n18\t5.47619059338547672811059907834101382489E-01\n19\t5.71428571428571428571428571428571428571E-01\n20\t6.01382488479262672811059907834101382489E-01\n21\t6.19815668202764976958525345622119815668E-01\n22\t6.24423963133640552995391705069124423963E-01\n23\t6.40552995391705069124423963133640552995E-01\n24\t6.56682027649769585253456221198156682028E-01\n25\t6.7281105990783410138248847926267281106E-01\n26\t6.84331797235023041474654377880184331797E-01\n27\t6.98156682027649769585253456221198156682E-01\n28\t7.16589861751152073732718894009216589862E-01\n29\t7.30414746543778801843317972350230414747E-01\n30\t7.48847926267281105990783410138248847926E-01\n31\t7.60368663594470046082949308755760368664E-01\n32\t7.71889400921658986175115207373271889401E-01\n33\t7.85714285714285714285714285714285714286E-01\n34\t8.00691244239631336405529953917050691244E-01\n35\t8.04147465437788018433179723502304147465E-01\n36\t8.06451612903225806451612903225806451613E-01\n37\t8.20276497695852534562211981566820276498E-01\n38\t8.31797235023041474654377880184331797235E-01\n39\t8.38709677419354838709677419354838709677E-01\n40\t8.38709677419354838709677419354838709677E-01\n41\t8.38709677419354838709677419354838709677E-01\n42\t8.43317972350230414746543778801843317972E-01\n43\t8.47926267281105990783410138248847926267E-01\n44\t8.59447004608294930875576036866359447005E-01\n45\t8.66359447004608294930875576036866359447E-01\n46\t8.75576036866359447004608294930875576037E-01\n47\t8.82488479262672811059907834101382488479E-01\n48\t8.84792626728110599078341013824884792627E-01\n49\t8.98617511520737327188940092165898617512E-01\n50\t9.03225806451612903225806451612903225807E-01\n51\t9.12442396313364055299539170506912442396E-01\n52\t9.14746543778801843317972350230414746544E-01\n53\t9.19354838709677419354838709677419354839E-01\n54\t9.26267281105990783410138248847926267281E-01\n55\t9.35483870967741935483870967741935483871E-01\n56\t9.37788018433179723502304147465437788018E-01\n57\t9.42396313364055299539170506912442396313E-01\n58\t9.44700460829493087557603686635944700461E-01\n59\t9.47004608294930875576036866359447004608E-01\n60\t9.56221198156682027649769585253456221198E-01\n61\t9.60829493087557603686635944700460829493E-01\n62\t9.63133640552995391705069124423963133641E-01\n63\t9.65437788018433179723502304147465437788E-01\n64\t9.67741935483870967741935483870967741936E-01\n65\t9.70046082949308755760368663594470046083E-01\n66\t9.7235023041474654377880184331797235023E-01\n67\t9.74654377880184331797235023041474654378E-01\n68\t9.79262672811059907834101382488479262673E-01\n69\t9.8156682027649769585253456221198156682E-01\n70\t9.83870967741935483870967741935483870968E-01\n71\t9.83870967741935483870967741935483870968E-01\n72\t9.83870967741935483870967741935483870968E-01\n73\t9.86175115207373271889400921658986175115E-01\n74\t9.88479262672811059907834101382488479263E-01\n75\t9.88479262672811059907834101382488479263E-01\n76\t9.88479262672811059907834101382488479263E-01\n77\t9.88479262672811059907834101382488479263E-01\n78\t9.88479262672811059907834101382488479263E-01\n79\t9.88479262672811059907834101382488479263E-01\n80\t9.88479262672811059907834101382488479263E-01\n81\t9.88479262672811059907834101382488479263E-01\n82\t9.88479262672811059907834101382488479263E-01\n83\t9.9078341013824884792626728110599078341E-01\n84\t9.9078341013824884792626728110599078341E-01\n85\t9.93087557603686635944700460829493087558E-01\n86\t9.93087557603686635944700460829493087558E-01\n87\t9.93087557603686635944700460829493087558E-01\n88\t9.93087557603686635944700460829493087558E-01\n89\t9.93087557603686635944700460829493087558E-01\n90\t9.95391705069124423963133640552995391705E-01\n91\t9.95391705069124423963133640552995391705E-01\n92\t9.95391705069124423963133640552995391705E-01\n93\t9.95391705069124423963133640552995391705E-01\n94\t9.97695852534562211981566820276497695853E-01\n95\t9.97695852534562211981566820276497695853E-01\n96\t9.97695852534562211981566820276497695853E-01\n97\t1\n98\t1\n99\t1\n100\t1\n","type":"TAB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ROUND(PREDICTION_PROBABILITY(SVM_CLASS_MODEL, '1' USING "," '3' AS HOUSEHOLD_SIZE, "," 5 AS YRS_RESIDENCE, "," 1 AS Y_BOX_GAMES),3) PROBABILITY_AFFINITY_CARD_RESPONDER","FROM DUAL;"," "],"enabled":true,"result":{"startTime":1715312813766,"interpreter":"sql.medium","endTime":1715312813844,"results":[{"message":"PROBABILITY_AFFINITY_CARD_RESPONDER\n0.182\n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":7,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"table","title":"Get prediction details","message":["%sql","","SELECT CUST_ID,"," round(PREDICTION_YRS_RES,3) PRED_YRS_RES,"," RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute1\",17,100)),'rank=\"1\"/>') FIRST_ATTRIBUTE,"," RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute2\",17,100)),'rank=\"2\"/>') SECOND_ATTRIBUTE,"," RTRIM(TRIM(SUBSTR(OUTPRED.\"Attribute3\",17,100)),'rank=\"3\"/>') THIRD_ATTRIBUTE","FROM (SELECT CUST_ID,"," PREDICTION(SVM_CLASS_MODEL USING *) PREDICTION_YRS_RES,"," PREDICTION_DETAILS(SVM_CLASS_MODEL USING *) PD"," FROM TRAIN_DATA_CLAS"," ORDER BY CUST_ID) OUT,"," XMLTABLE('/Details'"," PASSING OUT.PD"," COLUMNS "," \"Attribute1\" XMLType PATH 'Attribute[1]',"," \"Attribute2\" XMLType PATH 'Attribute[2]',"," \"Attribute3\" XMLType PATH 'Attribute[3]') OUTPRED","FETCH FIRST 10 ROWS ONLY;"],"enabled":true,"result":{"startTime":1715312813933,"interpreter":"sql.medium","endTime":1715312816639,"results":[{"message":"CUST_ID\tPRED_YRS_RES\tFIRST_ATTRIBUTE\tSECOND_ATTRIBUTE\tTHIRD_ATTRIBUTE\n100002\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".913\" \t\"FLAT_PANEL_MONITOR\" actualValue=\"1\" weight=\".066\" \t\"HOME_THEATER_PACKAGE\" actualValue=\"1\" weight=\".036\" \n100003\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".839\" \t\"HOME_THEATER_PACKAGE\" actualValue=\"0\" weight=\".018\" \t\"FLAT_PANEL_MONITOR\" actualValue=\"1\" weight=\".016\" \n100005\t0\t\"BULK_PACK_DISKETTES\" actualValue=\"0\" weight=\".337\" \t\"YRS_RESIDENCE\" actualValue=\"5\" weight=\".013\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".011\" \n100007\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".024\" \t\t\n100008\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".029\" \t\t\n100009\t0\t\"Y_BOX_GAMES\" actualValue=\"1\" weight=\".638\" \t\"BULK_PACK_DISKETTES\" actualValue=\"0\" weight=\".38\" \t\"YRS_RESIDENCE\" actualValue=\"3\" weight=\".373\" \n100011\t0\t\"OCCUPATION\" actualValue=\"Farming\" weight=\".084\" \t\"YRS_RESIDENCE\" actualValue=\"3\" weight=\".007\" \t\"BULK_PACK_DISKETTES\" actualValue=\"0\" weight=\"-.002\" \n100013\t0\t\"BULK_PACK_DISKETTES\" actualValue=\"0\" weight=\".088\" \t\"HOME_THEATER_PACKAGE\" actualValue=\"1\" weight=\".027\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".016\" \n100015\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"2\" weight=\".898\" \t\"FLAT_PANEL_MONITOR\" actualValue=\"1\" weight=\".428\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".025\" \n100016\t0\t\"HOUSEHOLD_SIZE\" actualValue=\"9+\" weight=\".756\" \t\"HOME_THEATER_PACKAGE\" actualValue=\"0\" weight=\".003\" \t\"Y_BOX_GAMES\" actualValue=\"0\" weight=\".002\" \n","type":"TABLE"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","## Create Classification Model using Settings Table","","The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model."],"enabled":true,"result":{"startTime":1715312816725,"interpreter":"md.medium","endTime":1715312816789,"results":[{"message":"<h2 id=\"create-classification-model-using-settings-table\">Create Classification Model using Settings Table<\/h2>\n<p>The settings table is an alternative way to specify algorithm settings to build the model. We first drop the settings table, create it, and then populate it with settings before building the model.<\/p>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":"[{\"raw\":{\"height\":300,\"lastColumns\":[],\"version\":1}}]","hideInIFrame":false,"selectedVisualization":"raw","title":"Build classification model","message":["%script","","BEGIN EXECUTE IMMEDIATE 'DROP TABLE N1_BUILD_SETTINGS PURGE';","EXCEPTION WHEN OTHERS THEN NULL; END;","/","CREATE TABLE N1_BUILD_SETTINGS (setting_name VARCHAR2(30),setting_value VARCHAR2(4000));","/","BEGIN DBMS_DATA_MINING.DROP_MODEL('CLASS_MODEL_2');","EXCEPTION WHEN OTHERS THEN NULL; END;","/","","BEGIN"," INSERT INTO N1_BUILD_SETTINGS (setting_name, setting_value) VALUES ('ALGO_NAME', 'ALGO_SUPPORT_VECTOR_MACHINES');"," INSERT INTO N1_BUILD_SETTINGS (setting_name, setting_value) VALUES ('PREP_AUTO', 'ON');",""," DBMS_DATA_MINING.CREATE_MODEL("," MODEL_NAME => 'CLASS_MODEL_2', "," MINING_FUNCTION => 'CLASSIFICATION', "," DATA_TABLE_NAME => 'TRAIN_DATA_CLAS', "," CASE_ID_COLUMN_NAME => 'CUST_ID',"," TARGET_COLUMN_NAME => 'AFFINITY_CARD', "," SETTINGS_TABLE_NAME => 'N1_BUILD_SETTINGS');"," DBMS_OUTPUT.PUT_LINE ('Created model: CLASS_MODEL_2 ');","END;"],"enabled":true,"result":{"startTime":1715312816868,"interpreter":"script.medium","endTime":1715312818966,"results":[{"message":"\nPL/SQL procedure successfully completed.\n\n\n---------------------------\n\nTable N1_BUILD_SETTINGS created.\n\n\n---------------------------\n\n---------------------------\n\nPL/SQL procedure successfully completed.\n\n\n---------------------------\nCreated model: CLASS_MODEL_2 \n\n\nPL/SQL procedure successfully completed.\n\n","type":"TEXT"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":false,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":true,"hideVizConfig":false,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md","","## End of Script"],"enabled":true,"result":{"startTime":1715312819051,"interpreter":"md.medium","endTime":1715312819112,"results":[{"message":"<h2 id=\"end-of-script\">End of Script<\/h2>\n","type":"HTML"}],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":false,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"},{"col":0,"visualizationConfig":null,"hideInIFrame":false,"selectedVisualization":"html","title":null,"message":["%md"],"enabled":true,"result":{"startTime":1715312819198,"interpreter":"md.medium","endTime":1715312819259,"results":[],"taskStatus":"SUCCESS","forms":"[]","status":"SUCCESS"},"sizeX":0,"hideCode":true,"width":12,"hideResult":true,"dynamicFormParams":null,"row":0,"hasTitle":false,"hideVizConfig":true,"hideGutter":true,"relations":[],"forms":"[]"}],"version":"6","snapshot":false,"tags":null}]
