由于形状问题，使用多项式回归拟合模型不允许预测

Question

我写了下面的代码来使用多项式回归。能够拟合模型，但无法预测！！

def polynomial_function(power=5, random_state=9):
    global X_train
    global y_train

    X_train =  X_train[['item_1','item_2','item_3','item_4']]
    rng = np.random.RandomState(random_state)
    poly = PolynomialFeatures(degree=power, include_bias=False)
    linreg = LinearRegression(normalize=True)
    new_X_train = poly.fit_transform(X_train)
    linreg.fit(new_X_train, y_train)
    new_x_test  = np.array([4, 5, 6, 7]).reshape(1, -1)
    print linreg.predict(new_x_test)
    return linreg

linreg = polynomial_function()

收到以下错误消息：

ValueError: shapes (1,4) and (125,) not aligned: 4 (dim 1) != 125 (dim 0)

错误发生在这里，

new_x_test  = np.array([4, 5, 6, 7]).reshape(1, -1)
print linreg.predict(new_x_test)

我找到了 new_X_train = (923, 125) 的形状 new_x_test = (1, 4)

的形状

这有什么关系？

当我尝试使用 (1, 4) 的形状进行预测时，算法是否会尝试将其转换为不同的形状？

是否尝试为测试数据找出5次多项式？

我正在尝试学习多项式回归，谁能解释一下发生了什么？

Answer 1

from sklearn.pipeline import Pipeline
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures

pipeline = Pipeline([
    ('poly', PolynomialFeatures(degree=5, include_bias=False)),
    ('linreg', LinearRegression(normalize=True))
    ])

pipeline.fit(X_train, y_train)
pipeline.predict(np.array([4, 5, 6, 7]).reshape(1, -1))

由于形状问题，使用多项式回归拟合模型不允许预测

Fitting a model using Polynomial Regression doesn't allow to predict because of the shape issue

python

machine-learning

scikit-learn

data-science