AutoML之flaml：基于flaml框架对比lightgbm和xgboost模型进行自动化参数调优实现体内脂肪含量值回归预测案例之详细攻略

Posted 2023-03-18 一个处女座的程序猿

AutoML之flaml：基于flaml框架对比lightgbm和xgboost模型进行自动化参数调优实现体内脂肪含量值回归预测案例之详细攻略

 

目录

基于flaml框架对比lightgbm和xgboost模型进行自动化参数调优实现体内脂肪含量值回归预测案例

# 1、定义数据集

# 3、模型训练与验证

# 3.1、切分数据集

# 3.2、模型建立及训练

# 设定参数

# 3.3、输出最佳参数及其loss、耗费时间

# 3.4、模型评估

# 3.5、输出模型特征重要性(针对xgboost算法)

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AutoML之flaml：基于flaml框架对比lightgbm和xgboost模型进行自动化参数调优实现体内脂肪含量值回归预测案例之详细攻略
AutoML之flaml：基于flaml框架对比lightgbm和xgboost模型进行自动化参数调优实现体内脂肪含量值回归预测案例实现代码​​​​​​​

基于flaml框架对比lightgbm和xgboost模型进行自动化参数调优实现体内脂肪含量值回归预测案例

# 1、定义数据集

	Density	Age	Weight	Height	Neck	Chest	Abdomen	Hip	Thigh	Knee	Ankle	Biceps	Forearm	Wrist	class
26	1.0811	34	131.5	67.5	36.2	88.6	74.6	85.3	51.7	34.7	21.4	28.7	27	16.5	7.9
11	1.0812	27	216	76	39.4	103.6	90.9	107.7	66.2	39.2	25.9	37.2	30.2	19	7.8
62	1.0298	54	193.25	70.25	38	107.6	102.4	99.4	61	39.4	23.6	32.7	29.9	19.1	30.7
54	1.0906	42	136.25	67.5	37.8	87.6	77.6	88.6	51.9	34.9	22.5	27.7	27.5	18.5	3.9
72	1.0796	56	160.75	73.75	36.4	93.6	82.9	96.3	52.9	37.5	23.1	29.7	27.3	18.2	8.5
234	1.0403	60	157.75	67.5	40.4	97.2	93.3	94	54.3	35.7	21	31.3	28.7	18.3	25.8
127	1.059	43	152.25	67.75	37.5	95.9	78	93.2	53.5	35.8	20.8	33.9	28.2	17.4	17.4
241	1.0207	65	224.5	68.25	38.8	119.6	118	114.3	61.3	42.1	23.4	34.9	30.1	19.4	35
212	1.0543	49	168.25	71.75	38.3	98.3	89.7	99.1	56.3	38.8	23	29.5	27.9	18.6	19.5
137	1.0325	43	187.75	74	37.7	97.8	98.6	100.6	63.6	39.2	23.8	34.3	28.4	17.7	29.4
169	1.061	35	172.75	69.5	37.6	99.1	90.8	98.1	60.1	39.1	23.4	32.5	29.8	17.4	16.5
219	1.0646	53	154.5	69.25	37.6	93.9	88.7	94.5	53.7	36.2	22	28.5	25.7	17.1	15
50	1.0756	47	158.25	72.25	34.9	90.2	86.7	98.3	52.6	37.2	22.4	26	25.8	17.3	10.2
248	1.0236	72	201	69.75	40.9	108.5	105	104.5	59.6	40.8	23.2	35.2	28.6	20.1	33.6
245	1.0641	68	155.5	69.25	36.3	97.4	84.3	94.4	54.3	37.5	22.6	29.2	27.3	18.5	15.2
184	1.0587	40	170.5	74.25	37.7	98.9	90.4	95.5	55.4	38.9	22.4	30.5	28.9	17.7	17.5
148	1.0873	25	143.75	72.5	35.2	92.3	76.5	92.1	51.9	35.7	22	25.8	25.2	16.9	5.3
226	1.065	55	169.5	68.25	37.2	101.7	91.1	97.1	56.6	38.5	22.6	33.4	29.3	18.8	14.8
95	1.0991	53	224.5	77.75	41.1	113.2	99.2	107.5	61.7	42.3	23.2	32.9	30.8	20.4	17.4
41	1.025	44	205	29.5	36.6	106	104.3	115.5	70.6	42.5	23.7	33.6	28.7	17.4	32.9

# 3、模型训练与验证

# 3.1、切分数据集

# 3.2、模型建立及训练

​​​​​​​# 设定参数

# 3.3、输出最佳参数及其loss、耗费时间

xgboost
Best hyperparmeter config: 'n_estimators': 62, 'max_leaves': 10, 'min_child_weight': 0.7892720199977752, 'learning_rate': 0.1101743533128382, 'subsample': 0.746247577681268, 'colsample_bylevel': 0.7152322660529653, 'colsample_bytree': 0.9299683267604371, 'reg_alpha': 0.0391736214683682, 'reg_lambda': 0.0043196018324903095
Best r2 on validation data: 0.9624
Training duration of best run: 0.03208 s



lgbm
Best hyperparmeter config: 'n_estimators': 43, 'num_leaves': 4, 'min_child_samples': 4, 'learning_rate': 0.6033971750435617, 'log_max_bin': 7, 'colsample_bytree': 0.9043858352550422, 'reg_alpha': 0.0009765625, 'reg_lambda': 32.27210014686686
Best r2 on validation data: 0.9586
Training duration of best run: 0.008558 s

# 3.4、模型评估

R2:  0.9853399901027871
MAE:  0.6854529804653592
MSE:  0.9898463799269194


R2:  0.9727251680203316
MAE:  1.0213410357069146
MSE:  1.8416013282039074

# 3.5、输出模型特征重要性(针对xgboost算法)