X 有 1 个特征，但 LinearRegression 需要 10 个特征作为输入

Question

我在这里看到过类似的问题，但它们似乎都是由不同的问题引起的。我试过重塑并确保它是一个二维数组，但我一直收到这个错误。这是我的代码：

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import KNeighborsRegressor
from io import StringIO
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn import linear_model
d = pd.read_csv("http://www.stat.wisc.edu/~jgillett/451/data/mtcars.csv")
X = d[['cyl','disp','hp','drat','wt','qsec','vs','am','gear','carb']]
y=d[['mpg']].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=0)
model = linear_model.LinearRegression()
model.fit(X_train, y_train)
pred = model.predict(X_test).reshape(-1,1)
y_test=y_test.reshape(-1,1)
model.score(pred,y_test)

我会很感激任何帮助！

Answer 1

这应该有效

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import KNeighborsRegressor
from io import StringIO
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn import linear_model
d = pd.read_csv("http://www.stat.wisc.edu/~jgillett/451/data/mtcars.csv")
X = d[['cyl','disp','hp','drat','wt','qsec','vs','am','gear','carb']]
y=d[['mpg']].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=0)
model = linear_model.LinearRegression()
model.fit(X_train, y_train)
pred = model.predict(X_test).reshape(-1,1)
y_test=y_test.reshape(-1,1)
model.score(X_test,y_test)

Answer 2

model.score()

的参数应该是

X_test

和

y_test

，而不是

pred

和

y_test

。

来自docs：

Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            Test samples. For some estimators this may be a precomputed
            kernel matrix or a list of generic objects instead with shape
            ``(n_samples, n_samples_fitted)``, where ``n_samples_fitted``
            is the number of samples used in the fitting for the estimator.
        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
            True values for `X`.