ML之CatboostC：基于titanic泰坦尼克数据集利用catboost算法实现二分类

目录

基于titanic泰坦尼克数据集利用catboost算法实现二分类

设计思路

输出结果

核心代码

相关内容
ML之CatBoost：CatBoost算法的简介、安装、案例应用之详细攻略
ML之CatboostC：基于titanic泰坦尼克数据集利用catboost算法实现二分类
ML之CatboostC：基于titanic泰坦尼克数据集利用catboost算法实现二分类实现

基于titanic泰坦尼克数据集利用catboost算法实现二分类

设计思路

输出结果

   Pclass     Sex   Age  SibSp  Parch  Survived
0       3    male  22.0      1      0         0
1       1  female  38.0      1      0         1
2       3  female  26.0      0      0         1
3       1  female  35.0      1      0         1
4       3    male  35.0      0      0         0
Pclass        int64
Sex          object
Age         float64
SibSp         int64
Parch         int64
Survived      int64
dtype: object
object_features_ID： [1]
0:	learn: 0.5469469	test: 0.5358272	best: 0.5358272 (0)	total: 98.1ms	remaining: 9.71s
1:	learn: 0.4884967	test: 0.4770551	best: 0.4770551 (1)	total: 98.7ms	remaining: 4.84s
2:	learn: 0.4459496	test: 0.4453159	best: 0.4453159 (2)	total: 99.3ms	remaining: 3.21s
3:	learn: 0.4331858	test: 0.4352757	best: 0.4352757 (3)	total: 99.8ms	remaining: 2.4s
4:	learn: 0.4197131	test: 0.4266055	best: 0.4266055 (4)	total: 100ms	remaining: 1.91s
5:	learn: 0.4085381	test: 0.4224953	best: 0.4224953 (5)	total: 101ms	remaining: 1.58s
6:	learn: 0.4063807	test: 0.4209804	best: 0.4209804 (6)	total: 102ms	remaining: 1.35s
7:	learn: 0.4007713	test: 0.4155077	best: 0.4155077 (7)	total: 102ms	remaining: 1.17s
8:	learn: 0.3971064	test: 0.4135872	best: 0.4135872 (8)	total: 103ms	remaining: 1.04s
9:	learn: 0.3943774	test: 0.4105674	best: 0.4105674 (9)	total: 103ms	remaining: 928ms
10:	learn: 0.3930801	test: 0.4099915	best: 0.4099915 (10)	total: 104ms	remaining: 839ms
11:	learn: 0.3904409	test: 0.4089840	best: 0.4089840 (11)	total: 104ms	remaining: 764ms
12:	learn: 0.3890830	test: 0.4091666	best: 0.4089840 (11)	total: 105ms	remaining: 701ms
13:	learn: 0.3851196	test: 0.4108839	best: 0.4089840 (11)	total: 105ms	remaining: 647ms
14:	learn: 0.3833366	test: 0.4106298	best: 0.4089840 (11)	total: 106ms	remaining: 600ms
15:	learn: 0.3792283	test: 0.4126097	best: 0.4089840 (11)	total: 106ms	remaining: 558ms
16:	learn: 0.3765680	test: 0.4114997	best: 0.4089840 (11)	total: 107ms	remaining: 522ms
17:	learn: 0.3760966	test: 0.4112166	best: 0.4089840 (11)	total: 107ms	remaining: 489ms
18:	learn: 0.3736951	test: 0.4122305	best: 0.4089840 (11)	total: 108ms	remaining: 461ms
19:	learn: 0.3719966	test: 0.4101199	best: 0.4089840 (11)	total: 109ms	remaining: 435ms
20:	learn: 0.3711460	test: 0.4097299	best: 0.4089840 (11)	total: 109ms	remaining: 411ms
21:	learn: 0.3707144	test: 0.4093512	best: 0.4089840 (11)	total: 110ms	remaining: 389ms
22:	learn: 0.3699238	test: 0.4083409	best: 0.4083409 (22)	total: 110ms	remaining: 370ms
23:	learn: 0.3670864	test: 0.4071850	best: 0.4071850 (23)	total: 111ms	remaining: 351ms
24:	learn: 0.3635514	test: 0.4038399	best: 0.4038399 (24)	total: 111ms	remaining: 334ms
25:	learn: 0.3627657	test: 0.4025837	best: 0.4025837 (25)	total: 112ms	remaining: 319ms
26:	learn: 0.3621028	test: 0.4018449	best: 0.4018449 (26)	total: 113ms	remaining: 304ms
27:	learn: 0.3616121	test: 0.4011693	best: 0.4011693 (27)	total: 113ms	remaining: 291ms
28:	learn: 0.3614262	test: 0.4011820	best: 0.4011693 (27)	total: 114ms	remaining: 278ms
29:	learn: 0.3610673	test: 0.4005475	best: 0.4005475 (29)	total: 114ms	remaining: 267ms
30:	learn: 0.3588062	test: 0.4002801	best: 0.4002801 (30)	total: 115ms	remaining: 256ms
31:	learn: 0.3583703	test: 0.3997255	best: 0.3997255 (31)	total: 116ms	remaining: 246ms
32:	learn: 0.3580553	test: 0.4001878	best: 0.3997255 (31)	total: 116ms	remaining: 236ms
33:	learn: 0.3556808	test: 0.4004169	best: 0.3997255 (31)	total: 118ms	remaining: 228ms
34:	learn: 0.3536833	test: 0.4003229	best: 0.3997255 (31)	total: 119ms	remaining: 220ms
35:	learn: 0.3519948	test: 0.4008047	best: 0.3997255 (31)	total: 119ms	remaining: 212ms
36:	learn: 0.3515452	test: 0.4000576	best: 0.3997255 (31)	total: 120ms	remaining: 204ms
37:	learn: 0.3512962	test: 0.3997214	best: 0.3997214 (37)	total: 120ms	remaining: 196ms
38:	learn: 0.3507648	test: 0.4001569	best: 0.3997214 (37)	total: 121ms	remaining: 189ms
39:	learn: 0.3489575	test: 0.4009203	best: 0.3997214 (37)	total: 121ms	remaining: 182ms
40:	learn: 0.3480966	test: 0.4014031	best: 0.3997214 (37)	total: 122ms	remaining: 175ms
41:	learn: 0.3477613	test: 0.4009293	best: 0.3997214 (37)	total: 122ms	remaining: 169ms
42:	learn: 0.3472945	test: 0.4006602	best: 0.3997214 (37)	total: 123ms	remaining: 163ms
43:	learn: 0.3465271	test: 0.4007531	best: 0.3997214 (37)	total: 124ms	remaining: 157ms
44:	learn: 0.3461538	test: 0.4010608	best: 0.3997214 (37)	total: 124ms	remaining: 152ms
45:	learn: 0.3455060	test: 0.4012489	best: 0.3997214 (37)	total: 125ms	remaining: 146ms
46:	learn: 0.3449922	test: 0.4013439	best: 0.3997214 (37)	total: 125ms	remaining: 141ms
47:	learn: 0.3445333	test: 0.4010754	best: 0.3997214 (37)	total: 126ms	remaining: 136ms
48:	learn: 0.3443186	test: 0.4011180	best: 0.3997214 (37)	total: 126ms	remaining: 132ms
49:	learn: 0.3424633	test: 0.4016071	best: 0.3997214 (37)	total: 127ms	remaining: 127ms
50:	learn: 0.3421565	test: 0.4013135	best: 0.3997214 (37)	total: 128ms	remaining: 123ms
51:	learn: 0.3417523	test: 0.4009993	best: 0.3997214 (37)	total: 128ms	remaining: 118ms
52:	learn: 0.3415669	test: 0.4009101	best: 0.3997214 (37)	total: 129ms	remaining: 114ms
53:	learn: 0.3413867	test: 0.4010833	best: 0.3997214 (37)	total: 130ms	remaining: 110ms
54:	learn: 0.3405166	test: 0.4014830	best: 0.3997214 (37)	total: 130ms	remaining: 107ms
55:	learn: 0.3401535	test: 0.4015556	best: 0.3997214 (37)	total: 131ms	remaining: 103ms
56:	learn: 0.3395217	test: 0.4021097	best: 0.3997214 (37)	total: 132ms	remaining: 99.4ms
57:	learn: 0.3393024	test: 0.4023377	best: 0.3997214 (37)	total: 132ms	remaining: 95.8ms
58:	learn: 0.3389909	test: 0.4019616	best: 0.3997214 (37)	total: 133ms	remaining: 92.3ms
59:	learn: 0.3388494	test: 0.4019746	best: 0.3997214 (37)	total: 133ms	remaining: 88.9ms
60:	learn: 0.3384901	test: 0.4017470	best: 0.3997214 (37)	total: 134ms	remaining: 85.6ms
61:	learn: 0.3382250	test: 0.4018783	best: 0.3997214 (37)	total: 134ms	remaining: 82.4ms
62:	learn: 0.3345761	test: 0.4039633	best: 0.3997214 (37)	total: 135ms	remaining: 79.3ms
63:	learn: 0.3317548	test: 0.4050218	best: 0.3997214 (37)	total: 136ms	remaining: 76.3ms
64:	learn: 0.3306501	test: 0.4036656	best: 0.3997214 (37)	total: 136ms	remaining: 73.3ms
65:	learn: 0.3292310	test: 0.4034339	best: 0.3997214 (37)	total: 137ms	remaining: 70.5ms
66:	learn: 0.3283600	test: 0.4033661	best: 0.3997214 (37)	total: 137ms	remaining: 67.6ms
67:	learn: 0.3282389	test: 0.4034237	best: 0.3997214 (37)	total: 138ms	remaining: 64.9ms
68:	learn: 0.3274603	test: 0.4039310	best: 0.3997214 (37)	total: 138ms	remaining: 62.2ms
69:	learn: 0.3273430	test: 0.4041663	best: 0.3997214 (37)	total: 139ms	remaining: 59.6ms
70:	learn: 0.3271585	test: 0.4044144	best: 0.3997214 (37)	total: 140ms	remaining: 57.1ms
71:	learn: 0.3268457	test: 0.4046981	best: 0.3997214 (37)	total: 140ms	remaining: 54.6ms
72:	learn: 0.3266497	test: 0.4042724	best: 0.3997214 (37)	total: 141ms	remaining: 52.1ms
73:	learn: 0.3259684	test: 0.4048797	best: 0.3997214 (37)	total: 141ms	remaining: 49.7ms
74:	learn: 0.3257845	test: 0.4044766	best: 0.3997214 (37)	total: 142ms	remaining: 47.3ms
75:	learn: 0.3256157	test: 0.4047031	best: 0.3997214 (37)	total: 143ms	remaining: 45.1ms
76:	learn: 0.3251433	test: 0.4043698	best: 0.3997214 (37)	total: 144ms	remaining: 42.9ms
77:	learn: 0.3247743	test: 0.4041652	best: 0.3997214 (37)	total: 144ms	remaining: 40.6ms
78:	learn: 0.3224876	test: 0.4058880	best: 0.3997214 (37)	total: 145ms	remaining: 38.5ms
79:	learn: 0.3223339	test: 0.4058139	best: 0.3997214 (37)	total: 145ms	remaining: 36.3ms
80:	learn: 0.3211858	test: 0.4060056	best: 0.3997214 (37)	total: 146ms	remaining: 34.2ms
81:	learn: 0.3200423	test: 0.4067103	best: 0.3997214 (37)	total: 147ms	remaining: 32.2ms
82:	learn: 0.3198329	test: 0.4069039	best: 0.3997214 (37)	total: 147ms	remaining: 30.1ms
83:	learn: 0.3196561	test: 0.4067853	best: 0.3997214 (37)	total: 148ms	remaining: 28.1ms
84:	learn: 0.3193160	test: 0.4072288	best: 0.3997214 (37)	total: 148ms	remaining: 26.1ms
85:	learn: 0.3184463	test: 0.4077451	best: 0.3997214 (37)	total: 149ms	remaining: 24.2ms
86:	learn: 0.3175777	test: 0.4086243	best: 0.3997214 (37)	total: 149ms	remaining: 22.3ms
87:	learn: 0.3173824	test: 0.4082013	best: 0.3997214 (37)	total: 150ms	remaining: 20.4ms
88:	learn: 0.3172840	test: 0.4083946	best: 0.3997214 (37)	total: 150ms	remaining: 18.6ms
89:	learn: 0.3166252	test: 0.4086761	best: 0.3997214 (37)	total: 151ms	remaining: 16.8ms
90:	learn: 0.3164144	test: 0.4083237	best: 0.3997214 (37)	total: 151ms	remaining: 15ms
91:	learn: 0.3162137	test: 0.4083699	best: 0.3997214 (37)	total: 152ms	remaining: 13.2ms
92:	learn: 0.3155611	test: 0.4091627	best: 0.3997214 (37)	total: 152ms	remaining: 11.5ms
93:	learn: 0.3153976	test: 0.4089484	best: 0.3997214 (37)	total: 153ms	remaining: 9.76ms
94:	learn: 0.3139281	test: 0.4116939	best: 0.3997214 (37)	total: 154ms	remaining: 8.08ms
95:	learn: 0.3128878	test: 0.4146652	best: 0.3997214 (37)	total: 154ms	remaining: 6.42ms
96:	learn: 0.3127863	test: 0.4145767	best: 0.3997214 (37)	total: 155ms	remaining: 4.78ms
97:	learn: 0.3126696	test: 0.4142118	best: 0.3997214 (37)	total: 155ms	remaining: 3.17ms
98:	learn: 0.3120048	test: 0.4140831	best: 0.3997214 (37)	total: 156ms	remaining: 1.57ms
99:	learn: 0.3117563	test: 0.4138267	best: 0.3997214 (37)	total: 156ms	remaining: 0us
 
bestTest = 0.3997213503
bestIteration = 37
 
Shrink model to first 38 iterations.

核心代码

class CatBoostClassifier Found at: catboost.core
 
class CatBoostClassifier(-title class_ inherited__">CatBoost):
    _estimator_type = 'classifier'
    """
    Implementation of the scikit-learn API for CatBoost classification.
    Parameters
    ----------
    iterations : int, [default=500]
        Max count of trees.
        range: [1,+inf]
    learning_rate : float, [default value is selected automatically for 
     binary classification with other parameters set to default. In all 
     other cases default is 0.03]
        Step size shrinkage used in update to prevents overfitting.
        range: (0,1]
    depth : int, [default=6]
        Depth of a tree. All trees are the same depth.
        range: [1,+inf]
    l2_leaf_reg : float, [default=3.0]
        Coefficient at the L2 regularization term of the cost function.
        range: [0,+inf]
    model_size_reg : float, [default=None]
        Model size regularization coefficient.
        range: [0,+inf]
    rsm : float, [default=None]
        Subsample ratio of columns when constructing each tree.
        range: (0,1]
    loss_function : string or object, [default='Logloss']
        The metric to use in training and also selector of the machine 
         learning
        problem to solve. If string, then the name of a supported 
         metric,
        optionally suffixed with parameter description.
        If object, it shall provide methods 'calc_ders_range' or 
         'calc_ders_multi'.
    border_count : int, [default = 254 for training on CPU or 128 for 
     training on GPU]
        The number of partitions in numeric features binarization. 
         Used in the preliminary calculation.
        range: [1,65535] on CPU, [1,255] on GPU
    feature_border_type : string, [default='GreedyLogSum']
        The binarization mode in numeric features binarization. Used 
         in the preliminary calculation.
        Possible values:
            - 'Median'
            - 'Uniform'
            - 'UniformAndQuantiles'
            - 'GreedyLogSum'
            - 'MaxLogSum'