ML之DT:基于DT决策树算法(对比是否经特征筛选FS处理)对Titanic(泰坦尼克号)数据集进行二分类预测
目录
初步处理后的 X_train: (984, 474) (0, 0) 31.19418104265403 (0, 78) 1.0 (0, 82) 1.0 (0, 366) 1.0 (0, 391) 1.0 (0, 435) 1.0 (0, 437) 1.0 (0, 473) 1.0 (1, 0) 31.19418104265403 (1, 73) 1.0 (1, 79) 1.0 (1, 296) 1.0 (1, 389) 1.0 (1, 397) 1.0 (1, 436) 1.0 (1, 446) 1.0 (2, 0) 31.19418104265403 (2, 78) 1.0 (2, 82) 1.0 (2, 366) 1.0 (2, 391) 1.0 (2, 435) 1.0 (2, 437) 1.0 (2, 473) 1.0 (3, 0) 32.0 : : (980, 473) 1.0 (981, 0) 12.0 (981, 73) 1.0 (981, 81) 1.0 (981, 84) 1.0 (981, 390) 1.0 (981, 435) 1.0 (981, 436) 1.0 (981, 473) 1.0 (982, 0) 18.0 (982, 78) 1.0 (982, 81) 1.0 (982, 277) 1.0 (982, 390) 1.0 (982, 435) 1.0 (982, 437) 1.0 (982, 473) 1.0 (983, 0) 31.19418104265403 (983, 78) 1.0 (983, 82) 1.0 (983, 366) 1.0 (983, 391) 1.0 (983, 435) 1.0 (983, 436) 1.0 (983, 473) 1.0 | 经过FS处理后的 X_train_fs: (984, 94) (0, 93) 1.0 (0, 85) 1.0 (0, 83) 1.0 (0, 76) 1.0 (0, 71) 1.0 (0, 27) 1.0 (0, 24) 1.0 (0, 0) 31.19418104265403 (1, 84) 1.0 (1, 74) 1.0 (1, 63) 1.0 (1, 25) 1.0 (1, 19) 1.0 (1, 0) 31.19418104265403 (2, 93) 1.0 (2, 85) 1.0 (2, 83) 1.0 (2, 76) 1.0 (2, 71) 1.0 (2, 27) 1.0 (2, 24) 1.0 (2, 0) 31.19418104265403 (3, 93) 1.0 (3, 85) 1.0 (3, 83) 1.0 : : (980, 24) 1.0 (980, 0) 31.19418104265403 (981, 93) 1.0 (981, 84) 1.0 (981, 83) 1.0 (981, 75) 1.0 (981, 28) 1.0 (981, 26) 1.0 (981, 19) 1.0 (981, 0) 12.0 (982, 93) 1.0 (982, 85) 1.0 (982, 83) 1.0 (982, 75) 1.0 (982, 26) 1.0 (982, 24) 1.0 (982, 0) 18.0 (983, 93) 1.0 (983, 84) 1.0 (983, 83) 1.0 (983, 76) 1.0 (983, 71) 1.0 (983, 27) 1.0 (983, 24) 1.0 (983, 0) 31.19418104265403 |
- class SelectPercentile Found at: sklearn.feature_selection.univariate_selection
-
- class SelectPercentile(-title class_ inherited__">_BaseFilter):
- """Select features according to a percentile of the highest scores.
-
- Read more in the :ref:`User Guide <univariate_feature_selection>`.
-
- Parameters
- ----------
- score_func : callable
- Function taking two arrays X and y, and returning a pair of arrays
- (scores, pvalues) or a single array with scores.
- Default is f_classif (see below "See also"). The default function only
- works with classification tasks.
-
- percentile : int, optional, default=10
- Percent of features to keep.
-
- Attributes
- ----------
- scores_ : array-like, shape=(n_features,)
- Scores of features.
-
- pvalues_ : array-like, shape=(n_features,)
- p-values of feature scores, None if `score_func` returned only scores.
-
- Notes
- -----
- Ties between features with equal scores will be broken in an unspecified
- way.
-
- See also
- --------
- f_classif: ANOVA F-value between label/feature for classification tasks.
- mutual_info_classif: Mutual information for a discrete target.
- chi2: Chi-squared stats of non-negative features for classification tasks.
- f_regression: F-value between label/feature for regression tasks.
- mutual_info_regression: Mutual information for a continuous target.
- SelectKBest: Select features based on the k highest scores.
- SelectFpr: Select features based on a false positive rate test.
- SelectFdr: Select features based on an estimated false discovery rate.
- SelectFwe: Select features based on family-wise error rate.
- GenericUnivariateSelect: Univariate feature selector with configurable mode.
- """
- def __init__(self, score_func=f_classif, percentile=10):
- super(SelectPercentile, self).__init__(score_func)
- self.percentile = percentile
-
- def _check_params(self, X, y):
- if not 0 <= self.percentile <= 100:
- raise ValueError(
- "percentile should be >=0, <=100; got %r" % self.percentile)
-
- def _get_support_mask(self):
- check_is_fitted(self, 'scores_')
- Cater for NaNs
- if self.percentile == 100:
- return np.ones(len(self.scores_), dtype=np.bool)
- elif self.percentile == 0:
- return np.zeros(len(self.scores_), dtype=np.bool)
- scores = _clean_nans(self.scores_)
- treshold = stats.scoreatpercentile(scores,
- 100 - self.percentile)
- mask = scores > treshold
- ties = np.where(scores == treshold)[0]
- if len(ties):
- max_feats = int(len(scores) * self.percentile / 100)
- kept_ties = ties[:max_feats - mask.sum()]
- mask[kept_ties] = True
- return mask
网站声明:如果转载,请联系本站管理员。否则一切后果自行承担。
加入交流群
请使用微信扫一扫!