我用线性回归的简单预测不会执行

Question

下面是我的试用代码：

from sklearn import linear_model

# plt.title("Time-independent variant student performance analysis")

x_train = [5, 9, 33, 25, 4]
y_train = [35, 2, 14 ,9, 7]
x_test = [14, 2, 8, 1, 11]

# create linear regression object
linear = linear_model.LinearRegression()

#train the model using the training sets and check score
linear.fit(x_train, y_train)
linear.score(x_train, y_train)

# predict output
predicted = linear.predict(x_test)

当运行时，这是输出：

ValueError: Found arrays with inconsistent numbers of samples: [1 5]

Answer 1

重新定义

x_train = [[5],[9],[33],[25],[4]]
y_train = [35,2,14,9,7]
x_test = [[14],[2],[8],[1],[11]]

来自 fit(X, y) 的文档：X：numpy 数组或形状为 [n_samples,n_features]

的稀疏矩阵

在您的例子中，每个示例都只有一个特征。

我用线性回归的简单预测不会执行

my simple prediction with linear regression wont execute

python

regression

prediction

linear-regression

scikit-learn