我正在使用网格搜索来获得最合适的
k=['rbf', 'linear','poly','sigmoid']
c= [1,5,10,20,30,50,80,100]
g=[1e-7,1e-6,1e-5,1e-4,1e-2,0.0001]
param_grid=dict(kernel=k, C=c, gamma=g)
print (param_grid)
grid = GridSearchCV(SVC, param_grid,scoring='accuracy')
grid.fit(X_t_train, y_t_train)
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_)
我收到 TypeError: get_params() Missing 1 requiredpositional argument: 'self' in grid.fit()
最佳答案
出现此错误是因为估计器必须使用对象而不是类进行初始化。您需要执行以下任一操作:
grid = GridSearchCV(SVC(), param_grid, scoring='accuracy')
或者类似这样的东西:
clf = SVC()
grid = GridSearchCV(clf, param_grid, scoring='accuracy')
关于python-3.x - 我正在使用 GridSearchCV 并且 fit 给了我一个 TypeError : get_params() missing 1 required positional argument: 'self' ，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/46618669/