Python ValueError:估计器Lasso的参数alphas无效
这是我第一次在这里发帖 我是一名Python机器学习新手,我一直在Jupyter笔记本(v6.0.3)中使用Scikit Learn(v0.22.1)自学。如果你能帮我解决这个问题,我将非常高兴 我完全是从auto_examples_python/dataset/plot_cv_diabetes.py(可从scikit learn 0.22.1下载的文件)复制了这段代码,但这段代码并没有在我的Jupyter笔记本上运行:Python ValueError:估计器Lasso的参数alphas无效,python,machine-learning,scikit-learn,lasso-regression,gridsearchcv,Python,Machine Learning,Scikit Learn,Lasso Regression,Gridsearchcv,这是我第一次在这里发帖 我是一名Python机器学习新手,我一直在Jupyter笔记本(v6.0.3)中使用Scikit Learn(v0.22.1)自学。如果你能帮我解决这个问题,我将非常高兴 我完全是从auto_examples_python/dataset/plot_cv_diabetes.py(可从scikit learn 0.22.1下载的文件)复制了这段代码,但这段代码并没有在我的Jupyter笔记本上运行: import numpy as np import mat
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.linear_model import LassoCV, Lasso
from sklearn.model_selection import GridSearchCV, KFold
X, y = datasets.load_diabetes(return_X_y = True)
X = X[:150]
y = y[:150]
lasso = Lasso(alpha = 1.0, random_state = 0, max_iter = 10000)
alphas = np.logspace(-4, -0.5, 30)
tuned_parameters = [{'alphas': alphas}]
n_folds = 5
clf = GridSearchCV(lasso, tuned_parameters, cv=n_folds, refit = False)
clf.fit(X, y)
它给了我一个错误:
>ValueError: Invalid parameter alphas for estimator Lasso(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=10000,
normalize=False, positive=False, precompute=False, random_state=0,
selection='cyclic', tol=0.0001, warm_start=False). Check the list of available parameters with `estimator.get_params().keys()`.
当我这样做时:
scores = clf.cv_results_['mean_test_score']
scores_std = clf.cv_results_['std_test_score']
plt.figure().set_size_inches(8, 6)
plt.semilogx(alphas, score)
我得到:
>AttributeError: 'GridSearchCV' object has no attribute 'cv_results_'
谢谢您的帮助。根据您的需要,您应该使用alpha
。
事实上,修改:
tuned_parameters = [{'alphas': alphas}]
进入:
你的代码应该可以工作
tuned_parameters = [{'alpha': alphas}]