本文整理汇总了Python中sklearn.linear_model.RidgeCV.score方法的典型用法代码示例。如果您正苦于以下问题：Python RidgeCV.score方法的具体用法？Python RidgeCV.score怎么用？Python RidgeCV.score使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.linear_model.RidgeCV的用法示例。
在下文中一共展示了RidgeCV.score方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: regularizedreg
# 需要导入模块: from sklearn.linear_model import RidgeCV [as 别名]
# 或者: from sklearn.linear_model.RidgeCV import score [as 别名]
def regularizedreg(Xtrain,Xtest,ytrain,ytest):
Rclf = RidgeCV(alphas=[1,2,20,40,50]) # RidgeCV(alphas=[0.1, 1.0, 2.0, 4.0, 20.0], cv=None, fit_intercept=True, scoring=None, normalize=False)
Rclf.fit(Xtrain,ytrain);
print("Residual sum of squares: %.2f"
% np.mean((Rclf.predict(Xtest) - ytest) ** 2))
print('Regularization choosen, alpha = %.2f' % Rclf.alpha_);
print(' Coef values = ', Rclf.coef_);
print('Variance score: %.2f' % Rclf.score(Xtest, ytest))示例2: LinearRegression
# 需要导入模块: from sklearn.linear_model import RidgeCV [as 别名]
# 或者: from sklearn.linear_model.RidgeCV import score [as 别名]
X = X[:-predPeriod] #re-sizing the features for training
dataset.dropna(inplace=True) # get rid of naN for 'label' column
# create label
y = np.array(dataset['label'])
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.2, random_state=1)
# use linearRegression as algrithm
#clf = LinearRegression()
clf = RidgeCV (alphas =[0.1, 0.5, 1, 10])
clf.fit(X_train, y_train)
#start_time = time.time()
y_pred = clf.predict(X_pred)
#print time.time() - start_time
accuracy = clf.score(X_test, y_test)
# visualize Learning Curves
#ML.ModelLearning(X, y)
#ML.ModelComplexity(X_train, y_train)
#Linear slope calculation
#print clf.alpha_
#print clf
#print clf.coef_
#print clf.intercept_
print 'predict accuracy is: {:0.2f}'.format(accuracy)
# build a column in data for predict result
data['predict/Adj Close'] = data['Adj Close'] # add column for predict value/Adj Close
示例3: print
# 需要导入模块: from sklearn.linear_model import RidgeCV [as 别名]
# 或者: from sklearn.linear_model.RidgeCV import score [as 别名]
print(ridge)
print("Percent variance explained: {0}".format(ridge.score(X_aging, y_aging)))
print("Coefficients found: \n{0}\n".format(prettyprint(ridge.coef_, col, sort=True)))
print("ORDINARY LEAST SQUARES")
print(ols)
print("Percent variance explained: {0}".format(ols.score(X_aging, y_aging)))
print("Coefficients found: \n{0}\n".format(prettyprint(ols.coef_, col, sort=True)))
print("WHOLE DATASET //////////////////////////")
print("SUPER AGERS //////////////////////////")
ridge = RidgeCV(alphas=alpha_params, cv=7, scoring=score)
ridge.fit(X_sa, y_sa)
ols = LinearRegression()
ols.fit(X_sa, y_sa)
print("RIDGE REGRESSION")
print("Percent variance explained: {0}".format(ridge.score(X_sa, y_sa)))
print("Coefficients found: \n{0}\n".format(prettyprint(ridge.coef_, col, sort=True)))
print("ORDINARY LEAST SQUARES")
print("Percent variance explained: {0}".format(ols.score(X_sa, y_sa)))
print("Coefficients found: \n{0}\n".format(prettyprint(ols.coef_, col, sort=True)))
print("SUPER AGERS //////////////////////////")
print("MCIS //////////////////////////")
ridge = RidgeCV(alphas=alpha_params, cv=7, scoring=score)
ridge.fit(X_mci, y_mci)
ols = LinearRegression()
ols.fit(X_mci, y_mci)
print("RIDGE REGRESSION")
print("Percent variance explained: {0}".format(ridge.score(X_mci, y_mci)))
print("Coefficients found: \n{0}\n".format(prettyprint(ridge.coef_, col, sort=True)))
print("ORDINARY LEAST SQUARES")