fit(X_train, y_train, eval_set=[(X_train, y_train), (X_test, y_test)], eval_metric=rmsle) # Train with custom objective clf = xgb.XGBRegressor(n_estimators=kBoostRound, objective=squared_log_obj, tree_method='hist', seed=kSeed) clf.fit(X_train, y_train, eval_set=[(X_train...
我使用不同的eta值来检查它对模型的影响。我的密码是- for eta in np.arange(0.2, 0.51, 0.03): xgb_model = xgboost.XGBClassifier(objective = 'multi:softmax', num_class = 5, eta = eta) xgb_model.fit(x_train, y_train) xgb_out = xgb_model.predict(x_test) print("For eta %f, 浏览...
model.fit(x_train, y_train, eval_set = [(x_test,y_test)], eval_metric = "mlogloss", early_stopping_rounds = 10, verbose = True) ### plot feature importance fig,ax = plt.subplots(figsize=(15,15)) plot_importance(model, height=0.5, ax=ax, max_num_features=64) plt.show() ##...
# setup parameters for xgboost param = {} param['booster'] = 'gbtree' param['objective'] = 'binary:logistic' param["eval_metric"] = "error" param['eta'] = 0.3 param['gamma'] = 0 param['max_depth'] = 6 param['min_child_weight']=1 param['max_delta_step'] = 0 param['subs...
由于我是通过anaconda来安装的Jupyter Notebook,所以首先需要解决Anaconda2(Python2)和Anaconda3(Python3)...
# 解决样本个数不平衡的问题42random_state=27 # 随机数43)44model.fit(x_train,45y_train,46eval_set = [(x_test,y_test)],47eval_metric = "mlogloss",48early_stopping_rounds = 10,49verbose = True)5051### plot feature importance52fig,ax = plt.subplots(figsize=(15,15))53plot_importance...
algorithms = [ svm.LinearSVC(), # <<<=== Works linear_model.RidgeClassifier(), # <<<=== Works XGBClassifier(), # <<<=== Works XGBClassifier(objective='multi:softprob', num_class=len(groups_count_dict), eval_metric='merror') # <<<=== Not working ] def train(algorithm...
params={'objective':'binary:logistic','eval_metric':['auc','error'],'max_depth':3,'learning_rate':0.1} Output: 0.90 xgb.train - ROUND (0) [0] validation_0-auc:0.941881 validation_0-error:0.070175 [1] validation_0-auc:0.937865 validation_0-error:0.070175 [2] validation_0-auc:0.97488...
multi:softprob num_class=n 返回概率 rank:pairwise eval_metric 回归任务(默认rmse)rmse--均⽅根误差 mae--平均绝对误差 分类任务(默认error)auc--roc曲线下⾯积 error--错误率(⼆分类)merror--错误率(多分类)logloss--负对数似然函数(⼆分类)mlogloss--负对数似然函数(多分类)gamma ...
# setup parameters for xgboost param = {} param['booster'] = 'gbtree' param['objective'] = 'binary:logistic' param["eval_metric"] = "error" param['eta'] = 0.3 param['gamma'] = 0 param['max_depth'] = 6 param['min_child_weight']=1 param['max_delta_step'] = 0 param['subs...