82.02247191011235955056179775280898876404에서 85.39325842696629213483146067415730337079로 상승
#1
DROP TABLE TIT_TRAIN;
CREATE TABLE TIT_TRAIN
AS
SELECT *
FROM TIT
WHERE CASENO < 802;
DROP TABLE TIT_TEST;
CREATE TABLE TIT_TEST
AS
SELECT *
FROM TIT
WHERE CASENO >= 802;
#2
DROP TABLE SETTINGS_GLM;
CREATE TABLE SETTINGS_GLM
AS
SELECT *
FROM TABLE (DBMS_DATA_MINING.GET_DEFAULT_SETTINGS)
WHERE SETTING_NAME LIKE '%GLM%';
BEGIN
INSERT INTO SETTINGS_GLM
VALUES (DBMS_DATA_MINING.ALGO_NAME, 'ALGO_RANDOM_FOREST');
INSERT INTO SETTINGS_GLM
VALUES (DBMS_DATA_MINING.PREP_AUTO, 'ON');
INSERT INTO SETTINGS_GLM
VALUES (DBMS_DATA_MINING.GLMS_REFERENCE_CLASS_NAME, 'GLMS_RIDGE_REG_DISABLE');
COMMIT;
END;
/
#3
BEGIN
DBMS_DATA_MINING.DROP_MODEL( 'MD_CLASSIFICATION_MODEL');
END;
/
#4
BEGIN
DBMS_DATA_MINING.CREATE_MODEL(
model_name => 'MD_CLASSIFICATION_MODEL',
mining_function => DBMS_DATA_MINING.CLASSIFICATION,
data_table_name => 'TIT_TRAIN',
case_id_column_name => 'CASENO',
target_column_name => 'ALIVE',
settings_table_name => 'SETTINGS_GLM');
END;
/
#5
SELECT MODEL_NAME,
ALGORITHM,
MINING_FUNCTION
FROM ALL_MINING_MODELS
WHERE MODEL_NAME = 'MD_CLASSIFICATION_MODEL';
#6
SELECT SETTING_NAME, SETTING_VALUE
FROM ALL_MINING_MODEL_SETTINGS
WHERE MODEL_NAME = 'MD_CLASSIFICATION_MODEL';
#7
SELECT CASENO, ALIVE as 실제값, PREDICTION (MD_CLASSIFICATION_MODEL USING *) 예측값
FROM TIT_TEST
ORDER BY CASENO;
#8
SELECT SUM(DECODE(실제값, 예측값, 1, 0)) / COUNT(*) * 100
FROM (SELECT CASENO, ALIVE AS 실제값,
PREDICTION (MD_CLASSIFICATION_MODEL USING *) 예측값
FROM TIT_TEST ORDER BY CASENO);
85.39325842696629213483146067415730337079