运行套索回归方法时出错

Posted 2023-03-12

【中文标题】运行套索回归方法时出错

【英文标题】：I got an error while running lasso regression method

【发布时间】：2021-12-10 19:41:54

【问题描述】：

raise NotFittedError(msg % 'name': type(estimator).name) sklearn.exceptions.NotFittedError：此套索实例未安装 然而。在使用它之前使用适当的参数调用“fit” 估算器。

from sklearn import datasets
from sklearn.linear_model import Lasso
from sklearn.model_selection import train_test_split
#
# Load the Boston Data Set
#
bh = datasets.load_boston()
X = bh.data
y = bh.target
#
# Create training and test split
#
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
#
# Create an instance of Lasso Regression implementation
#
lasso = Lasso(alpha=1.0)
#
# Fit the Lasso model
#
lasso.fit(X_test, y_test)
#
# Create the model score
#
#lasso.score(X_test, y_test), lasso.score(X_train, y_train)
lasso_reg = Lasso(normalize=True)
y_pred_lass =lasso_reg.predict(X_test)
print(y_pred_lass)

【问题讨论】：

您在运行lasso.fit之后创建了一个新的Lasso 对象，因此旧的套索对象及其拟合结果将丢失。

旧的套索对象实际上并没有丢失，它只是存储在不同的变量中。

【参考方案1】：

正如错误所说，您必须在调用 lasso_reg.predict(X_test) 之前调用 lasso_reg.fit(X_test, y_test) 这将解决问题。

lasso_reg = Lasso(normalize=True)
lasso_reg.fit(X_test, y_test)
y_pred_lass =lasso_reg.predict(X_test)
print(y_pred_lass)

【参考方案2】：

您实际上已经创建了套索模型。一个叫lasso，你适合。但在那之后，您创建了另一个lasso_reg = Lasso(normalize=True)，您尝试调用predict，但该模型尚未安装。试试这个：

from sklearn import datasets
from sklearn.linear_model import Lasso
from sklearn.model_selection import train_test_split

bh = datasets.load_boston()
X = bh.data
y = bh.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

lasso = Lasso(alpha=1.0, normalize=True)

lasso.fit(X_test, y_test)

y_pred_lass =lasso.predict(X_test)
print(y_pred_lass)