feature_indices list | feature_names list | cv_brier_mean float64 | cv_brier_per_fold list | target_brier float64 | config dict | n_samples int64 | trained_at timestamp[s] | trained_on string |
|---|---|---|---|---|---|---|---|---|
[
0,
3,
5,
8,
9,
10,
12,
15,
16,
19,
20,
22,
23,
25,
28,
31,
32,
35,
39,
40,
41,
44,
46,
47,
49,
53,
55,
57,
58,
61,
63,
66,
68,
71,
74,
77,
79,
85,
87,
92,
95,
97,
99,
102,
104,
105,
106,
116,
120,
122,
124,
125,
... | [
"polling_00",
"polling_03",
"polling_05",
"polling_08",
"polling_09",
"polling_10",
"polling_12",
"economic_01",
"economic_02",
"economic_05",
"economic_06",
"economic_08",
"economic_09",
"economic_11",
"sentiment_00",
"sentiment_03",
"sentiment_04",
"sentiment_07",
"sentiment_11... | 0.232742 | [
0.2391199240078463,
0.22768658996353658,
0.24624865049577768,
0.2232916721658544,
0.2273650957679327
] | 0.202386 | {
"model_type": "random_forest",
"n_estimators": 50,
"max_depth": 7,
"min_samples_leaf": 1,
"max_features_ratio": 0.4,
"feature_indices": [
0,
3,
5,
8,
9,
10,
12,
15,
16,
19,
20,
22,
23,
25,
28,
31,
32,
35,
39,
40,
41,
4... | 650 | 2026-05-03T03:04:12 | kaggle |
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