grid search参数调优

本文主要是介绍grid search参数调优，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

一、简介

之前看论文的时候，看到过grid search进行超参调优，一开始觉得应该是个比较高大上的东西，其实后来一看就是个暴力搜索。如果一个模型一共有3个参数，分别是A、B、C，假如A有3个选择，B有4个选择，C有5个选择，为了选出最好的模型，就需要一个参数一个参数试过去，从这60（$3\ast 4\ast 5$）个模型里面找出一个符合自己需求的。

注意：grid search是一种调优的思路，和实现方式是没啥关系的。

你既可以通过简单的for循环，也可以直接用sklearn里面的类sklearn.model_selection.GridSearchCV。

这篇博客我觉得讲得挺好的：调参必备–Grid Search网格搜索

grid search缺点也很明显，比较耗时，所以在实际应用中，需要减少调优参数的个数并且缩小每个参数的搜索范围。

二、代码实现

sklearn官网提供的代码就挺好。sklearn官网参数调优示例

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 SVCprint(__doc__)# 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)
X = digits.images.reshape((n_samples, -1))
y = digits.target# Split the dataset in two equal parts
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
tuned_parameters = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],'C': [1, 10, 100, 1000]},{'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]scores = ['precision', 'recall']for score in scores:print("# Tuning hyper-parameters for %s" % score)print()clf = GridSearchCV(SVC(), tuned_parameters, scoring='%s_macro' % score)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()y_true, y_pred = y_test, clf.predict(X_test)print(classification_report(y_true, y_pred))print()# Note the problem is too easy: the hyperparameter plateau is too flat and the
# output model is the same for precision and recall with ties in quality.

三、其他

官网是全英文的，如果嫌麻烦，可以看看这篇博客：【GridSearchCV，CV调优超参数使用】【K最近邻分类器 KNeighborsClassifier 使用】【交叉验证】

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