江苏手机网站建设公司,域名查询ip解析,scala网站开发,wordpress 添加主题编辑网格搜索#xff08;Grid Search#xff09;#xff1a; 原理#xff1a;网格搜索通过预定义的参数组合进行穷举搜索#xff0c;评估每一种参数组合的性能#xff0c;选择性能最佳的参数组合。实现#xff1a;使用GridSearchCV类。示例代码#xff1a;from sklearn.mod… 网格搜索Grid Search 原理网格搜索通过预定义的参数组合进行穷举搜索评估每一种参数组合的性能选择性能最佳的参数组合。实现使用GridSearchCV类。示例代码from sklearn.model_selection import GridSearchCV
from sklearn.svm import SVCparam_grid {C: [0.1, 1, 10], kernel: [linear, rbf]}
grid_search GridSearchCV(SVC(), param_grid, cv5)
grid_search.fit(X_train, y_train)
print(grid_search.best_params_)随机搜索Randomized Search 原理随机搜索在预定义的参数空间中随机选择参数组合进行评估通常比网格搜索更快特别是在参数空间较大时。实现使用RandomizedSearchCV类。示例代码from sklearn.model_selection import RandomizedSearchCV
from sklearn.svm import SVC
from scipy.stats import uniformparam_dist {C: uniform(0.1, 10), kernel: [linear, rbf]}
random_search RandomizedSearchCV(SVC(), param_dist, n_iter10, cv5)
random_search.fit(X_train, y_train)
print(random_search.best_params_)贝叶斯优化Bayesian Optimization 原理贝叶斯优化通过构建一个代理模型如高斯过程来预测不同参数组合的性能并选择最有希望的参数组合进行评估。实现可以使用skopt库中的BayesSearchCV类。示例代码from skopt import BayesSearchCV
from sklearn.svm import SVCparam_space {C: (0.1, 10), kernel: [linear, rbf]}
bayes_search BayesSearchCV(SVC(), param_space, n_iter10, cv5)
bayes_search.fit(X_train, y_train)
print(bayes_search.best_params_)遗传算法Genetic Algorithms 原理遗传算法模拟自然选择和遗传过程通过交叉、变异等操作在参数空间中搜索最优解。实现可以使用deap库或其他遗传算法库。示例代码from deap import base, creator, tools, algorithms
from sklearn.svm import SVC
from sklearn.model_selection import cross_val_scoredef eval_params(params):model SVC(**params)score cross_val_score(model, X_train, y_train, cv5).mean()return score,creator.create(FitnessMax, base.Fitness, weights(1.0,))
creator.create(Individual, list, fitnesscreator.FitnessMax)toolbox base.Toolbox()
toolbox.register(attr_C, random.uniform, 0.1, 10)
toolbox.register(attr_kernel, random.choice, [linear, rbf])
toolbox.register(individual, tools.initCycle, creator.Individual,(toolbox.attr_C, toolbox.attr_kernel), n1)
toolbox.register(population, tools.initRepeat, list, toolbox.individual)
toolbox.register(evaluate, eval_params)
toolbox.register(mate, tools.cxTwoPoint)
toolbox.register(mutate, tools.mutGaussian, mu0, sigma1, indpb0.1)
toolbox.register(select, tools.selTournament, tournsize3)population toolbox.population(n10)
algorithms.eaSimple(population, toolbox, cxpb0.5, mutpb0.2, ngen10)
best_individual tools.selBest(population, 1)[0]
print(best_individual)
