grid搜索最优参数

GridSearchCV

详细地址：http://scikit-learn.org/stable/modules/generated/sklearn.grid_search.GridSearchCV.html#examples-using-sklearn-grid-search-gridsearchcv

具体实例：

 

# -*- coding: utf-8 -*-

""" Created on Mon Jun 15 15:30:30 2015 @author: Chaofn """

import numpy as np from sklearn import datasets from sklearn import cross_validation from sklearn.svm import SVR from sklearn.grid_search import GridSearchCV #Laod sample data

boston=datasets.load_boston() """ We can now quickly sample a training set while holding out 40% of the data for testing (evaluating) our predictor """ x_train,x_test,y_train,y_test=cross_validation.train_test_split( boston.data,boston.target,test_size=0.3,random_state=0) #Fit regression model

svr=GridSearchCV(SVR(kernel='rbf',gamma=0.1),cv=5, param_grid={"C":[1e0,1e1,1e2,1e3], "gamma":np.logspace(-2,2,5)}) svr.fit(x_train,y_train) #Predict

predict_targets=svr.predict(x_test) #Evalution

n_test_samples=len(y_test) error=np.linalg.norm(predict_targets-y_test,ord=1)/n_test_samples print("Mean Absolute Error is:%.3f" %(error)) pcc=np.corrcoef(predict_targets,y_test)[0,1] print ("Pearson Correlation Coefficient: %.4f" %(pcc))