使用Sklearn，返回错误结果的多项式回归

Question

我正在尝试使用Python Sklearn库进行多项式回归，但是我获得的结果与我从Excel中获得的结果大不相同。

代码：

def polynomial_regression(x_param, y_param):
    print(x_param)
    print(y_param)
    """create a polynomial regression graph"""
    # convert x_param features to a numpy array
    x_param = np.array(x_param)

    # save a PolynomialFeatures with degree of 3
    poly = PolynomialFeatures(degree=3, include_bias=False)

    # we fit and transform the numpy array x_param
    poly_features = poly.fit_transform(x_param.reshape(-1, 1))

    # create a LinearRegression instance
    poly_reg_model = LinearRegression()

    # we fit our model to our data
    # which means we train our models by introducing poly_features and y_params values
    poly_reg_model.fit(poly_features, y_param)

    # predict the response 'y_predicted' based on the poly_features and the coef it estimated
    y_predicted = poly_reg_model.predict(poly_features)

    # visualising our model
    plt.figure(figsize=(10, 6))
    plt.title(f"Polynomial regression, coef={poly_reg_model.coef_}", size=16)
    plt.scatter(x_param, y_param)
    plt.plot(x_param, y_predicted, c="red")
    plt.show()

结果：

预期结果：

现在结果应该看起来像这样吗？如果是这样，如果没有，我做错了什么？ 感谢您提前的帮助。

Answer 1

@adam demo_fighter：让我发布一个解决方案，以澄清@Andrey Lukyanenko的讲话。

问题是plt.plot（x_param，y_predialded，c =“ red”）。此绘图命令将连接X_Param的后续​​点，并通过行段进行Y_________。如果x_param中的值不是单调的，则可以创建图片中出现的曲折ZAG模式。该解决方案只是在进行分析之前仅订购X值列表。

import matplotlib.pyplot as plt
import numpy as np
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression

# Original data
xraw = [3, 6, 6, 9, 12, 15, 3, 6, 13, 9, 13, 16]
yraw = [9, 12, 9, 9, 12, 9, 6, 3, 8, 3, 1, 3]
# Ordered data in x:
OrderedID   = np.argsort(xraw)
x = np.array(xraw)[OrderedID]
y = np.array(yraw)[OrderedID]

print(x)
print(y)

def polynomial_regression(x_param, y_param):
    """create a polynomial regression graph"""
    # save a PolynomialFeatures with degree of 3
    poly = PolynomialFeatures(degree=3, include_bias=False)
    print(poly)

    # we fit and transform the numpy array x_param
    poly_features = poly.fit_transform(x_param.reshape(-1, 1))

    # create a LinearRegression instance
    poly_reg_model = LinearRegression()

    # we fit our model to our data
    # which means we train our models by introducing poly_features and y_params values
    temp = poly_reg_model.fit(poly_features, y_param)
    print(temp)

    # predict the response 'y_predicted' based on the poly_features and the coef it estimated
    y_predicted = poly_reg_model.predict(poly_features)

    # visualising our model
    plt.figure(figsize=(10, 6))
    plt.title(f"Polynomial regression, coef={poly_reg_model.coef_}", size=16)
    plt.scatter(x_param, y_param)
    plt.plot(x_param, y_predicted, c="red")
    plt.show()

polynomial_regression(x, y)

PS1：我转换为NP.Array在功能之外。

ps2：不错的个人资料pic =）。