feature_importances_
1、feature_importances_方法的解释
XGBRegressor( ).feature_importances_
参数
注意：特性重要性只定义为树增强器。只有在选择决策树模型作为基础时，才定义特征重要性。
学习器(“助推器= gbtree”)。它不定义为其他基本的学习者类型，如线性学习者 (`booster=gblinear`).。
返回
feature_importances_: ' ' [n_features] ' '形状的数组
注意：importance_type: string, default "gain", The feature importance type for the feature_importances_ property: either "gain", "weight", "cover", "total_gain" or "total_cover".
2、feature_importances_的原生代码
class XGBModel(XGBModelBase):
# pylint: disable=too-many-arguments, too-many-instance-attributes, invalid-name
"""Implementation of the Scikit-Learn API for XGBoost.
Parameters
----------
max_depth : int
Maximum tree depth for base learners.
learning_rate : float
Boosting learning rate (xgb's "eta")
n_estimators : int
Number of boosted trees to fit.
silent : boolean
Whether to print messages while running boosting.
objective : string or callable
Specify the learning task and the corresponding learning objective or
a custom objective function to be used (see note below).
booster: string
Specify which booster to use: gbtree, gblinear or dart.
nthread : int
Number of parallel threads used to run xgboost. (Deprecated, please use ``n_jobs``)
n_jobs : int
Number of parallel threads used to run xgboost. (replaces ``nthread``)
gamma : float
Minimum loss reduction required to make a further partition on a leaf node of the tree.
min_child_weight : int
Minimum sum of instance weight(hessian) needed in a child.
max_delta_step : int
Maximum delta step we allow each tree's weight estimation to be.
subsample : float
Subsample ratio of the training instance.
colsample_bytree : float
Subsample ratio of columns when constructing each tree.
colsample_bylevel : float
Subsample ratio of columns for each split, in each level.
reg_alpha : float (xgb's alpha)
L1 regularization term on weights
reg_lambda : float (xgb's lambda)
L2 regularization term on weights
scale_pos_weight : float
Balancing of positive and negative weights.
base_score:
The initial prediction score of all instances, global bias.
seed : int
Random number seed. (Deprecated, please use random_state)
random_state : int
Random number seed. (replaces seed)
missing : float, optional
Value in the data which needs to be present as a missing value. If
None, defaults to np.nan.
importance_type: string, default "gain"
The feature importance type for the feature_importances_ property: either "gain",
"weight", "cover", "total_gain" or "total_cover".
\*\*kwargs : dict, optional
Keyword arguments for XGBoost Booster object. Full documentation of parameters can
be found here: https://github.com/dmlc/xgboost/blob/master/doc/parameter.rst.
Attempting to set a parameter via the constructor args and \*\*kwargs dict simultaneously
will result in a TypeError.
.. note:: \*\*kwargs unsupported by scikit-learn
\*\*kwargs is unsupported by scikit-learn. We do not guarantee that parameters
passed via this argument will interact properly with scikit-learn.
Note
----
A custom objective function can be provided for the ``objective``
parameter. In this case, it should have the signature
``objective(y_true, y_pred) -> grad, hess``:
y_true: array_like of shape [n_samples]
The target values
y_pred: array_like of shape [n_samples]
The predicted values
grad: array_like of shape [n_samples]
The value of the gradient for each sample point.
hess: array_like of shape [n_samples]
The value of the second derivative for each sample point
"""
def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100,
silent=True, objective="reg:linear", booster='gbtree',
n_jobs=1, nthread=None, gamma=0, min_child_weight=1, max_delta_step=0,
subsample=1, colsample_bytree=1, colsample_bylevel=1,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
base_score=0.5, random_state=0, seed=None, missing=None,
importance_type="gain", **kwargs):
if not SKLEARN_INSTALLED:
raise XGBoostError('sklearn needs to be installed in order to use this module')
self.max_depth = max_depth
self.learning_rate = learning_rate
self.n_estimators = n_estimators
self.silent = silent
self.objective = objective
self.booster = booster
self.gamma = gamma
self.min_child_weight = min_child_weight
self.max_delta_step = max_delta_step
self.subsample = subsample
self.colsample_bytree = colsample_bytree
self.colsample_bylevel = colsample_bylevel
self.reg_alpha = reg_alpha
self.reg_lambda = reg_lambda
self.scale_pos_weight = scale_pos_weight
self.base_score = base_score
self.missing = missing if missing is not None else np.nan
self.kwargs = kwargs
self._Booster = None
self.seed = seed
self.random_state = random_state
self.nthread = nthread
self.n_jobs = n_jobs
self.importance_type = importance_type
def feature_importances_(self):
"""
Feature importances property
.. note:: Feature importance is defined only for tree boosters
Feature importance is only defined when the decision tree model is chosen as base
learner (`booster=gbtree`). It is not defined for other base learner types, such
as linear learners (`booster=gblinear`).
Returns
-------
feature_importances_ : array of shape ``[n_features]``
"""
if getattr(self, 'booster', None) is not None and self.booster != 'gbtree':
raise AttributeError(
'Feature importance is not defined for Booster type {}'.format(self.booster))
b = self.get_booster()
score = b.get_score(importance_type=self.importance_type)
all_features = [score.get(f, 0.) for f in b.feature_names]
all_features = np.array(all_features, dtype=np.float32)
return all_features / all_features.sum()