2、objective:目标函数的第一部分(即衡量损失的部分) 3、alpha&lambda:参数化决策树 4、gamma:复杂性控制/防止过拟合/让树停止生长 (四)其他过程 1、剪枝参数:减轻过拟合 2、分类样本不均衡问题 (五)参数总结 1、num_round/n_estimators 2、slient 3、subsample 4、eta/learning_rate 5、xgb_model/booster...
xgb.train():objective: 默认reg:squarederror xgb.XGBRegressor() :objective: 默认reg:squarederror xgb.XGBClassifier() : objective: 默认binary:logistic reg:squarederror 均方误差,回归时使用 reg:squaredlogerror 均方对数损失,回归时使用 reg:logistic 逻辑回归,二分类时使用 binary:logistic 二分类时候使用的逻辑...
#for i in range(len(x_train)): # numerator+=(x_train[i]-x_mean)*(y_train[i]-y_mean) # distance=(x_train[i]-x_mean)**2 //方法二向量化,更简单快速 numerator=(x_train-x_mean).dot(y_train-y_mean) distance=(x_train-x_mean).dot(x_train-x_mean) self.a_=numerator/distance...
任务参数 tweedie回归的参数(Objective=reg: Twitdie) Pseudo-Huber的参数(reg:pseudohubererror) 命令行参数 全局配置 可以使用 py:func:‘xgboost.config_context()’(Python)或 xgb.set.config()®在全局范围内设置以下参数。 verbosity: 过程输出信息的打印。0(silent), 1(warning), 2(info), 3(debug) u...
objective 目标函数的选择要根据问题确定,如果是回归问题 ,一般是 reg:linear , reg:logistic , count:poisson 如果是分类问题,一般是binary:logistic ,rank:pairwise 参数初步定之后划分20%为验证集,准备一个watchlist 给train和validation set ,设置num_round 足够大(比如100000),以至于你能发现每一个round 的验证...
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1234565) # set XGBoost's parameters params = { 'booster': 'gbtree', 'objective': 'multi:softmax', # 回归任务设置为:'objective': 'reg:gamma', 'num_class': 3, # 回归任务没有这个参数 '...
X_dtrain,X_deval,y_dtrain,y_deval=cross_validation.train_test_split(X_train,y_train,random_state=1026,test_size=0.3)dtrain=xgb.DMatrix(X_dtrain,y_dtrain)deval=xgb.DMatrix(X_deval,y_deval)watchlist=[(deval,'eval')]params={'booster':'gbtree','objective':'reg:linear','subsample':0....
watchlist = [(xg_train,'train'), (xg_test,'test')]# 定义参数params = {'objective':'multi:softmax','eta':0.1,'max_depth':9,'eval_metric':'merror','seed':0,'missing': -999,'class_num': class_num,'silent':1, }# 训练bst = xgb.train(params, xg_train,60, watchlist, early_...
XGBRegressor(max_depth=6, # 可以调节这些参数来改进模型效果 learning_rate=0.12, n_estimators=90, min_child_weight=6, objective="reg:gamma") model.fit(x_train, y_train) 1 2 3 4 5 6 7 8 9 特征重要性图像尺寸调整 import xgboost as xgb from xgboost import plot_importance from matplotlib...
# xgb格式数据 d_train = xgb.DMatrix(train_df, label=train_label) d_dev = xgb.DMatrix(dev_df, label=dev_label) # xgb参数 param = {'max_depth':4, 'eta':1, 'silent':0, 'objective':'multi:softmax', 'num_class':5} num_round = 20 # 模型训练 model = xgb.train(param, d_tra...