选择关于 k 折交叉验证的最佳模型

Question

我想获取 Iris 数据并根据 GridSearchCV 函数选择最佳逻辑模型。

我目前的工作

import numpy as np
from sklearn import datasets
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression

iris = datasets.load_iris()
X = iris.data[:, :2]
y = iris.target

# Logistic regression 
reg_log = LogisticRegression()

# Penalties
pen = ['l1', 'l2','none']

#Regularization strength (numbers from -10 up to 10)
C = np.logspace(-10, 10, 100)

# Possibilities for those parameters
parameters= dict(C=C, penalty=pen)

# choosing best model based on 5-fold cross validation
Model = GridSearchCV(reg_log, parameters, cv=5)

# Fitting best model
Best_model = Model.fit(X, y)

而且我遇到了很多错误。你知道我做错了什么吗？

Answer 1

由于你选择了不同的正则化，你可以在help page上看到：

The ‘newton-cg’, ‘sag’, and ‘lbfgs’ solvers support only L2 regularization with primal formulation, or no regularization. The ‘liblinear’ solver supports both L1 and L2 regularization, with a dual formulation only for the L2 penalty. The Elastic-Net regularization is only supported by the ‘saga’ solver.

我不太确定你是否想使用 penalization = 'none' 和 penalization 分数进行网格搜索。因此，如果您使用 saga 并增加迭代：

reg_log = LogisticRegression(solver="saga",max_iter=1000)

pen = ['l1', 'l2']
C = [0.1,0.001]

parameters= dict(C=C, penalty=pen)

Model = GridSearchCV(reg_log, parameters, cv=5)

Best_model = Model.fit(X, y)

res = pd.DataFrame(Best_model.cv_results_)
res[['param_C','param_penalty','mean_test_score']]

    param_C param_penalty   mean_test_score
0   0.1 l1  0.753333
1   0.1 l2  0.833333
2   0.001   l1  0.333333
3   0.001   l2  0.700000

它工作得很好。如果您的惩罚值出现更多错误.. 尝试查看它们并确保它们不是一些疯狂的值。

选择关于 k 折交叉验证的最佳模型

Choosing best model regarding to k-fold cross validation

python

numpy

machine-learning

scikit-learn

cross-validation