我正在学习交叉验证-网格搜索，并发现了这个 youtube 播放列表，该教程也已上传到 github 中作为 ipython 笔记本。我试图重新创建“同时搜索多个参数”部分的代码，但是我使用的是 SVM 回归，而不是 KNN。这是我的代码。
from sklearn.datasets import load_iris
from sklearn import svm
from sklearn.grid_search import GridSearchCV
import matplotlib.pyplot as plt
import numpy as np
iris = load_iris()
X = iris.data
y = iris.target
k=['rbf', 'linear','poly','sigmoid','precomputed']
c= range(1,100)
g=np.arange(1e-4,1e-2,0.0001)
g=g.tolist()
param_grid=dict(kernel=k, C=c, gamma=g)
print param_grid
svr=svm.SVC()
grid = GridSearchCV(svr, param_grid, cv=5,scoring='accuracy')
grid.fit(X, y)
print()
print("Grid scores on development set:")
print()
print grid.grid_scores_
print("Best parameters set found on development set:")
print()
print(grid.best_params_)
print("Grid best score:")
print()
print (grid.best_score_)
# create a list of the mean scores only
grid_mean_scores = [result.mean_validation_score for result in grid.grid_scores_]
print grid_mean_scores
但是它会产生这个错误
raise ValueError("X should be a square kernel matrix") ValueError: X should be a square kernel matrix