python使用GBDT输出f1指标

要输出GBDT模型的f1指标，需要进行以下步骤：

1. 导入所需的库和数据

import pandas as pd
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.metrics import f1_score

data = load_breast_cancer()
X = pd.DataFrame(data.data, columns=data.feature_names)
y = pd.Series(data.target)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

2. 创建并训练GBDT模型

gbdt = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1, max_depth=3, random_state=42)
gbdt.fit(X_train, y_train)

3. 对测试集进行预测并计算f1指标

y_pred = gbdt.predict(X_test)
f1 = f1_score(y_test, y_pred)
print("f1 score: {:.2f}".format(f1))

完整代码如下：

import pandas as pd
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.metrics import f1_score

data = load_breast_cancer()
X = pd.DataFrame(data.data, columns=data.feature_names)
y = pd.Series(data.target)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

gbdt = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1, max_depth=3, random_state=42)
gbdt.fit(X_train, y_train)

y_pred = gbdt.predict(X_test)
f1 = f1_score(y_test, y_pred)
print("f1 score: {:.2f}".format(f1))

注意：在实际应用中，为了更准确地评估模型性能，应该使用交叉验证等更加严格的方法进行评估。