sklearn基于make_scorer函数构建自定义损失函数或者评估指标

Posted 2021-08-31 Data+Science+Insight

sklearn基于make_scorer函数构建自定义损失函数或者评估指标

# 导入需要的包和库

# Load libraries
from sklearn.metrics import make_scorer, r2_score
from sklearn.model_selection import train_test_split
from sklearn.linear_model import Ridge
from sklearn.datasets import make_regression

# 使用make_regression函数构建仿真数据；

# 使用train_test_split函数进行数据划分；

# Generate features matrix and target vector
X, y = make_regression(n_samples = 100,
                          n_features = 3,
                          random_state = 1)

# Create training set and test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.10, random_state=1)

# 构建岭回归模型并训练；

# Create ridge regression object
classifier = Ridge()

# Train ridge regression model