ML之回归预测：利用十(xgboost,10-1)种机器学习算法对无人驾驶汽车系统参数(2017年的data,18+2)进行回归预测值VS真实值——bug调试记录
ML之回归预测：利用十(xgboost,10-1)种机器学习算法对无人驾驶汽车系统参数(2017年的data,18+2)进行回归预测值VS真实值——bug调试记录
输出结果
1、增加XGBR算法
1、增加XGBR算法时候，采用网格搜索的方法
XGBR_grid_model Training time： 135.60037931849538
输出XGBR_grid_model模型的最优参数：
{'learning_rate': 0.03, 'max_depth': 4, 'n_estimators': 100}
XGBR_grid_model_best_score: 0.7993051604810518
XGBR_grid_model_score： -0.339632045000555
2、增加XGBR算法时候，调用得到的最佳参数，却输不出最佳参数对应的准确度！
3、增加XGBR算法时候，对新数据进行预测，遇到bug：因为XGBR算法，要求传入的数据都为数值型
成功解决ValueError: DataFrame.dtypes for label must be int, float or bool
4、默认配置参数，输出的准确度较低：54%
(1)、通过特征选择可得到最高准确度为67.69%
ML之回归预测：利用十种机器学习(LiR+xgboost(特征重要性+特征选择))算法对无人驾驶汽车系统参数(2017年的data,18+2)进行回归预测值VS真实值
XGBR：The value of default parameter of XGBR is 0.539021005879261
XGBR：R-squared value of DecisionTreeRegressor: 0.539021005879261
XGBR：测试[12.8, 13.0]行数据，
[53.609283 53.47569 53.920986 53.920986 53.61446 53.61446 53.826157
53.773552 53.44194 53.09419 54.089035 54.353115 53.321796 53.293243
53.970306 53.752117 53.460567 53.44237 53.840027 53.920986 52.35085
57.61133 57.598843 57.911274 58.06042 57.945023 57.665913]
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XGBR_model.feature_importances_：
[0.08985916 0.01444405 0.08940411 0. 0.03163605 0.01870877
0.00869713 0.12159647 0.03933521 0.12161936 0.02289704 0.04260873
0.02663714 0.04179822 0.03441375 0.14182347 0.01409451 0.14042689]
Thresh=0.000, n=18, Accuracy: 52.56%
Thresh=0.009, n=17, Accuracy: 52.56%
Thresh=0.014, n=16, Accuracy: 51.94%
Thresh=0.014, n=15, Accuracy: 52.89%
Thresh=0.019, n=14, Accuracy: 54.23%
Thresh=0.023, n=13, Accuracy: 53.38%
Thresh=0.027, n=12, Accuracy: 52.86%
Thresh=0.032, n=11, Accuracy: 53.08%
Thresh=0.034, n=10, Accuracy: 64.67%
Thresh=0.039, n=9, Accuracy: 67.69%
Thresh=0.042, n=8, Accuracy: 66.25%
Thresh=0.043, n=7, Accuracy: 67.52%
Thresh=0.089, n=6, Accuracy: 64.64%
Thresh=0.090, n=5, Accuracy: 40.05%
Thresh=0.122, n=4, Accuracy: 37.93%
Thresh=0.122, n=3, Accuracy: 19.23%
Thresh=0.140, n=2, Accuracy: 22.15%
Thresh=0.142, n=1, Accuracy: 11.77%
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