xgboost是基于GBDT原理进行改进的算法,效率高,并且可以进行并行化运算,而且可以在训练的过程中给出各个特征的评分,从而表明每个特征对模型训练的重要性, 调用的源码就不准备详述,本文主要侧重的是计算的原理,函数get_fscore源码如下,源码来自安装包:xgboost/python-package/xgboost/core.py 通过下面的源码可以看出,特征评...
调⽤的源码就不准备详述，本⽂主要侧重的是计算的原理，函数get_fscore源码如下，源码来⾃安装包：xgboost/python- package/xgboost/core.py 通过下⾯的源码可以看出，特征评分可以看成是被⽤来分离决策树的次数。def get_fscore(self, fmap=''):"""Get feature importance of each feature.Parameter...
feat_imp = pd.Series(alg.booster().get_fscore()).sort_values(ascending=False) feat_imp.plot(kind='bar', title='Feature Importances') plt.ylabel('Feature Importance Score')#xgboost’s sklearn没有feature_importances,但是#get_fscore() 有相同的功能 1 2 3 4 5 6 7 8 9 10 11 12 13...
booster : Booster, XGBModel or dict. Booster or XGBModel instance, or dict taken by Booster.get_fscore() ax : matplotlib Axes, default None. Target axes instance. If None, new figure and axes will be created. grid : bool, Turn the axes grids on or off. Default is True (On). impo...
_score)) bst = xgb.train(params, d_train,tree_nums,watchlist,early_stopping_rounds=100, verbose_eval=10) #最优模型迭代次数去训练 # feat_imp = pd.Series(clf.booster().get_fscore()).sort_values(ascending=False) # #新版需要转换成dict or list # #feat_imp = pd.Series(dict(clf.get_...
label = dtrain.get_label() ratio = float(np.sum(label == 0)) / np.sum(label==1) param['scale_pos_weight'] = ratio return (dtrain, dtest, param) # 先做预处理,计算样本权重,再做交叉验证 (param, dtrain, num_round, nfold=5, metrics={'auc'}, seed = 0, fpreproc = fprepr...
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()...
import jieba import xgboost as xgb from sklearn.model_selection import train_test_split import numpy as np from gensim.models import Word2Vec # reorganize data def get_split_sentences(file_path): res_sen=[] with open(file_path) as f: for line in f: split_query=jieba.lcut(line.strip(...
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feature_score=model.get_fscore() feature_score=sorted(feature_score.items(),key=lambdax:x[1],reverse=True) fs=[] for(key,value)infeature_score: fs.append("{0},{1}\n".format(key,value)) withopen('xgb_feature_score.csv','w')asf: ...