XGBModel是XGBoost模型在Qlib中的实现,其函数头及__init__()函数如下: classXGBModel(Model,FeatureInt):"""XGBModel Model"""def__init__(self,**kwargs):self._params={}self._params.update(kwargs)self.model=None 我们发现,XGBModel同时继承了Model类和FeatureInt类,故需要重写Model类的fit()和predict...
5、xgb_model/booster 6、obj/objective 7、alpha&lambda/reg_alpha®_lambda 8、gamma 9、eval_metric 10、max_depth 11、colsample_bytree&colsample_bylevel&colsample_bynode 12、min_child_weight 13、scale_pos_weight 14、max_delta_step 15、n_jobs/nthread 16、base_score 17、random_state 18、mi...
raise ImportError('You must install graphviz to plot tree') if not isinstance(booster, (Booster, XGBModel)): raise ValueError('booster must be Booster or XGBModel instance') if isinstance(booster, XGBModel): booster = booster.get_booster() tree = booster.get_dump(fmap=fmap)[num_trees] tre...
raiseImportError('You must install matplotlib to plot importance') ifisinstance(booster,XGBModel): importance=booster.get_booster().get_score(
model=xgb.train(params,dtrain,num_boost_round=100000,evals=watchlist) 然后开始逐个调参了。 1、首先调整max_depth ,通常max_depth 这个参数与其他参数关系不大,初始值设置为10,找到一个最好的误差值,然后就可以调整参数与这个误差值进行对比。比如调整到8,如果此时最好的误差变高了,那么下次就调整到12;如果...
The XGB regression model, with an R2 value of 0.99 (~1), shows the predictive power of the model. Feature importance analysis demonstrates the significance of cellulose choice and the addition of chitosan in enhancing compressive strength. The correlation heatmap reveals positive associati...
运行model.fit(eval_set,eval_metric)并诊断您的首次运行,特别是n_estimators 优化max_depth参数。它表示每棵树的深度,这也是每棵树中使用的不同特征的最大数量。我建议从小的max_depth开始(例如3),然后将其递增1,并在没有性能提高的情况下停止。这将有助于简化模型并避免过度拟合 ...
model.pre_proba(tempdrop(columns = ['id']))[:, 1] frame = dftest[['id']].copy() 总结 对职员离职预测进行了深入的研究,采用了多种机器学习算法进行分类预测,包括逻辑回归、梯度提升、随机森林、XGBoost、CatBoost和LightGBM,并进行了交叉验证和可视化。
(X_train, y_train) num_rounds = 500 model = xgb.train(plst, dtrain, num_rounds) # 对测试集进行预测 dtest = xgb.DMatrix(X_test) ans = model.predict(dtest) # 计算准确率 cnt1 = 0 cnt2 = 0 for i in range(len(y_test)): if ans[i] == y_test[i]: cnt1 += 1 else: ...
import numpy as np from sklearn.datasets import load_breast_cancer from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score class Node: def __init__(self): self.feature = None # 分裂特征 self.threshold = None # 分裂点 self.left = None # 左子节点 ...