网格搜索-logistic多分类python

这里是一个使用网格搜索和logistic回归进行多分类预测的Python代码示例：

import numpy as np
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import accuracy_score

# 加载数据集
iris = load_iris()
X = iris.data
y = iris.target

# 定义logistic回归分类器
clf = LogisticRegression(solver='lbfgs', multi_class='auto', max_iter=1000, tol=1e-4, fit_intercept=False, C=1e6)

# 定义网格搜索的参数范围
param_grid = {
    'penalty': ['l1', 'l2'],
    'C': [0.1, 1, 10, 100],
    'fit_intercept': [True, False]
}

# 运行网格搜索
grid_search = GridSearchCV(estimator=clf, param_grid=param_grid, cv=5, scoring='accuracy')
grid_search.fit(X, y)

# 输出最优参数和分类准确率
best_params = grid_search.best_params_
best_score = grid_search.best_score_
print("Best parameters:", best_params)
print("Best accuracy:", best_score)

# 计算最优模型的预测结果和准确率
y_pred = grid_search.predict(X)
accuracy = accuracy_score(y, y_pred)
print("Accuracy:", accuracy)

# 输出最优模型的系数和截距
clf = LogisticRegression(solver='lbfgs', multi_class='auto', max_iter=1000, tol=1e-4, fit_intercept=False, **best_params)
clf.fit(X, y)
print("Coefficients:", clf.coef_)
print("Intercepts:", clf.intercept_)

这个代码示例使用了GridSearchCV类进行网格搜索，尝试找到logistic回归的最优参数组合，使得多分类模型的分类准确率最大。最后输出了最优参数、最优准确率、模型的准确率以及模型的系数和截距。这个示例只是一个简单的实现，实际应用中可能需要更复杂的模型和更严格的性能评估。