逻辑回归模型自动调参，自动机器学习

Question

能否利用自动机器学习技术实现对逻辑回归的调参呢？比如autogluon或者flaml。或者有什么自动的方法实现对逻辑回归模型的优化，Hyperopt也行。能否提供一个简单的代码进行参考？

Answer 1

 
 
from hyperopt import fmin, tpe, hp, Trials
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import cross_val_score
from sklearn.metrics import accuracy_score
from sklearn.datasets import make_classification
 
 
 
# 加载数据集
data = load_breast_cancer()
X = data.data
y = data.target
 
# 定义超参数搜索空间
space = {
    'C': hp.loguniform('C', -5, 5),
    'fit_intercept': hp.choice('fit_intercept', [True, False]),
    'solver': hp.choice('solver', ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'])
}
 
# 定义目标函数
def objective(params):
    X, y = make_classification(n_features=10, random_state=42)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    
    model = LogisticRegression(**params)
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    score = accuracy_score(y_test, y_pred)
    
    return -score
 
# 使用Hyperopt进行自动调参
best = fmin(objective, space, algo=tpe.suggest, max_evals=100)
 
print(best)