在 python 中保存多个不同的多项式回归对象

Saving multiple different polynomial regression objects in python

我正在尝试生成不同次数的多项式回归并保存模型对象。

import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import PolynomialFeatures
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error as MSE

df = pd.read_csv("kc_house_train_data(1).csv")

X = df['sqft_living'].values
Y = df['price'].values

lm = LinearRegression()

def Poly(X, degree):
    poly = PolynomialFeatures(degree = degree)
    poly_X = poly.fit_transform(X.reshape(-1,1))
    
    return poly_X

models = []

for i in range(15):
    poly_X = Poly(X, i+1)
    model = lm.fit(poly_X, Y)
    model.append(models)

其中 lm 是来自 sklearn 的 LinearRegression()。

我最终得到了一个模型列表,但所有模型都是 15 次多项式。不确定我做错了什么。

编辑:print(df[['sqft_living', 'price']].head(10).tostring()) 的输出:

       sqft_living      price
0             1180   221900.0
1             2570   538000.0
2              770   180000.0
3             1960   604000.0
4             1680   510000.0
5             5420  1225000.0
6             1715   257500.0
7             1060   291850.0
8             1780   229500.0
9             1890   323000.0
10            3560   662500.0

您必须在每次迭代时重新创建模型。尝试:

models = []

for i in range(15):
    poly_X = Poly(X, i+1)
    lm = LinearRegression()
    model = lm.fit(poly_X, Y)
    models.append(model)
>>> models[0].n_features_in_
2

>>> models[1].n_features_in_
3

>>> models[2].n_features_in_
4