为什么多项式回归对不同等级返回相同的结果？

Question

我有这个数据框，我想计算臭氧的多项式回归。我将 o3 作为 y 值传递，将日期作为 x 值传递。为什么我的多项式回归在 2 到 15 年级看起来一样？我比较了4年级和15年级，没有区别...我将得到的回归与CurveExpert软件进行比较，它们完全不同...如何解决问题并查看4年级和15年级之间的差异？

import matplotlib.pyplot as plt
import datetime as dt
import pandas as pd

# Importing the dataset
dataset = pd.read_csv('https://raw.githubusercontent.com/iulianastroia/csv_data/master/final_dataframe.csv')


dataset['day'] = pd.to_datetime(dataset['day'], dayfirst=True)
dataset = dataset.sort_values(by=['readable time'])
print(dataset.head())

group_by_df = pd.DataFrame([name, group.mean()["o3"]] for name, group in dataset.groupby('day'))
group_by_df.columns = ['day', "o3"]
group_by_df['day'] = pd.to_datetime(group_by_df['day'])
group_by_df['day'] = group_by_df['day'].map(dt.datetime.toordinal)
X = group_by_df[['day']].values
y = group_by_df[['o3']].values

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)


# Fitting Linear Regression to the dataset
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(X, y)

# Visualizing the Linear Regression results
def viz_linear():
    plt.scatter(X, y, color='red')
    plt.plot(X, lin_reg.predict(X), color='blue')
    plt.title('Linear Regression')
    plt.xlabel('Date')
    plt.ylabel('O3 levels')
    plt.show()
    return
viz_linear()

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree=15)
X_poly = poly_reg.fit_transform(X)
pol_reg = LinearRegression()
pol_reg.fit(X_poly, y)

# Visualizing the Polymonial Regression results
def viz_polymonial():
    plt.scatter(X, y, color='red')
    plt.plot(X, pol_reg.predict(poly_reg.fit_transform(X)), color='blue')
    plt.title('poly Regression grade 15')
    plt.xlabel('Date')
    plt.ylabel('O3 levels')
    plt.show()
    return
viz_polymonial()

Answer 1

你离得很近。干得好，你有很多事情要做。

我认为您想将这样的测试集拟合为线性：

# Fitting Linear Regression to the dataset
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(X_test, y_test)

多项式也是这样：

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree=15)
X_poly = poly_reg.fit_transform(X_test)
pol_reg = LinearRegression()
pol_reg.fit(X_poly, y_test)

现在曲线在视觉上显示出很大的不同