多项式线性回归，我哪里错了？

Question

我正在做一个课程作业，问题是：

编写一个函数，在训练数据 X_train 上拟合 0 到 9 度的多项式线性回归模型。对于每个模型，计算训练数据和测试数据的 R2R2（确定系数）回归分数，然后返回这两个数组都在一个元组中。

这个函数应该返回一个 numpy 数组的元组 (r2_train, r2_test）。两个数组的形状都应该是 (10,)

我的代码：` 从 sklearn.linear_model 导入线性回归

from sklearn.preprocessing import PolynomialFeatures
from sklearn.metrics.regression import r2_score


np.random.seed(0)
n = 15
x = np.linspace(0,10,n) + np.random.randn(n)/5
y = np.sin(x)+x/6 + np.random.randn(n)/10


X_train, X_test, y_train, y_test = train_test_split(x, y, random_state=0)
def answer_two():

def answer_two():
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.metrics.regression import r2_score

# Your code here


def r2_traintest(deg):  

    poly=PolynomialFeatures(deg)
    model=LinearRegression()
    X_f=poly.fit_transform(X_train.reshape(-1,1))

    a=model.fit(X_f,y_train)


    dee=a.predict(poly.fit_transform(X_train.reshape(-1,1)))

    deez=r2_score(dee,y_train)

    gin=a.predict(poly.transform(X_test.reshape(-1,1)))

    mint=r2_score(gin,y_test)

    return deez,mint


lst=[]
lsts=[]

for x in range(0,10,1):

    lst.append(r2_traintest(x)[0])

    lsts.append(r2_traintest(x)[1])


return (np.array(lst),np.array(lsts))

不幸的是，这给了我一个错误的答案，我是否遗漏了什么，请帮忙。

Answer 1

看来您正在反转 r2_score 函数中的参数。必须是r2_score(y_true, y_pred)

这是我的代码：

import numpy as np
from sklearn.preprocessing import PolynomialFeatures
from sklearn.metrics.regression import r2_score
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split

def fit_poly(deg):
    poly = PolynomialFeatures(deg)
    model = LinearRegression()
    X_poly = poly.fit_transform(X_train.reshape(-1, 1))
    model.fit(X_poly, y_train)

    y_pred_train = model.predict(poly.fit_transform(X_train.reshape(-1, 1)))
    r2_train = r2_score(y_train, y_pred_train)

    y_pred_test = model.predict(poly.transform(X_test.reshape(-1, 1)))
    r2_test = r2_score(y_test, y_pred_test)

    return r2_train, r2_test


np.random.seed(0)
n = 15
x = np.linspace(0,10,n) + np.random.randn(n)/5
y = np.sin(x)+x/6 + np.random.randn(n)/10

X_train, X_test, y_train, y_test = train_test_split(x, y, random_state=0)

lst=[]
lsts=[]

for x in range(0,10,1):
    lst.append(fit_poly(x)[0])
    lsts.append(fit_poly(x)[1])

print(lst, lsts)

结果是：

[0.0，0.4292457781234663，0.45109980444082465，0.5871995368779847，0.9194194471769304，0.97578641430682，0.9901823324795082，0.9935250927840416，0.996375453877599，0.9980370625664945] P>

[ - 0.4780864173714179，-0.45237104233936676，-0.0685698414991589，0.005331052945740433，0.7300494281871148，0.8770830091614791，0.9214093981415002，0.9202150411139083，0.6324795282222648，-0.645253216177847] P>

使用您的代码，值有时会高于 1。

顺便说一句，你的新版本代码更清晰，如你所见，我复制粘贴了很多:)