sklearn.GridSearchCV选择超参

左手的ㄟ右手 2021-10-30 04:38 214阅读 0赞

from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.model_selection import GridSearchCV from sklearn.metrics import classification_report from sklearn.svm import SVC # Loading the Digits dataset digits = datasets.load_digits() # To apply an classifier on this data, we need to flatten the image, to # turn the data in a (samples, feature) matrix: n_samples = len(digits.images) # 分别 取出 数据 与 标签 reshape的作用是把原始矩阵格式的像素数据转化为一行一个样本的形式 X = digits.images.reshape((n_samples, -1)) y = digits.target # Split the dataset in two equal parts # 分割测试数据与训练数据(注意这里已经分割了数据集) X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.5, random_state=0) # Set the parameters by cross-validation # 输入模型的超参由验证集来选择 # SVM主要的超参有类似于正则的系数和内核函数 tuned_parameters = [{ 'kernel': ['rbf'], 'gamma': [1e-3, 1e-4], 'C': [1, 10, 100, 1000]}, { 'kernel': ['linear'], 'C': [1, 10, 100, 1000]}] #观察角度分别有准度与回归 scores = ['precision', 'recall'] for score in scores: print("# Tuning hyper-parameters for %s" % score) print() # 通过 GridSearchCV 搜索最佳的超参数 clf = GridSearchCV(SVC(), tuned_parameters, cv=5, scoring='%s_macro' % score) # 这里进行交叉验证的数据是之前分割的训练数据 # 而交叉验证本身又会分割数据，所以交叉验证这里分割的测试集我么可以看做为验证集，用来拟合模型的超参 clf.fit(X_train, y_train) print("Best parameters set found on development set:") print() print(clf.best_params_) print() print("Grid scores on development set:") print() means = clf.cv_results_['mean_test_score'] stds = clf.cv_results_['std_test_score'] for mean, std, params in zip(means, stds, clf.cv_results_['params']): print("%0.3f (+/-%0.03f) for %r" % (mean, std * 2, params)) print() print("Detailed classification report:") print() print("The model is trained on the full development set.") print("The scores are computed on the full evaluation set.") print() # 注意最后利用测试集展示的才是泛化误差 y_true, y_pred = y_test, clf.predict(X_test) print(classification_report(y_true, y_pred)) print() #关于验证集与交叉验证的关系，解释链接 https://www.jianshu.com/p/67010cba1834

转载于:https://www.cnblogs.com/lvdongjie/p/11314130.html