我是机器学习的新手,在将标量数组转换为2d数组时,我还面临一些问题。我试图在spyder中实现多项式回归。这是我的密码,请帮忙!
# Polynomial Regression
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataset = pd.read_csv('Position_Salaries.csv')
X = dataset.iloc[:, 1:2].values
y
我正在编写一个python代码,用于使用函数sin(2.pi.x)在0,1的范围内调查过拟合。我首先通过使用mu=0和sigma=1使用高斯分布添加一些随机噪声来生成N个数据点。我使用M次多项式拟合模型。以下是我的代码
import matplotlib.pyplot as plt
import numpy as np
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
# generate N random points
N=30
X=
我正在实现简单的多项式回归来预测给定大小的视频的时间,这是我自己的数据集。现在,由于某些原因,我的图得到了多个踪迹。
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataset = pd.read_csv('estSize.csv')
X = dataset.iloc[:, 0].values.reshape(-1,1)
y = dataset.iloc[:, 1].values.
对于这段用python和sklearn实现的多项式回归代码 import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
import pandas as pd
m=100
X=6*np.random.rand(m,1)-3
y=0.5*X**3+X+2+np.random.rand(m,1)
poly_features=PolynomialFe
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn import metrics
from sklearn.preprocessing import PolynomialFeatures
df = pd.read_csv("C:\\Users\\MONSTER\\Desktop\\dosyalar\\datasets\\Auto.csv")
x = df
我试图创建一个函数,用均方根误差为我的多项式回归模型找到最佳的n度,所以我不必一次又一次地手工拟合模型,然后绘制结果,但是我得到'x和y必须有相同的第一维,但是当我试图创建这个图时,有形状(10,1)和(1,‘)错误。
这是我的密码:
from sklearn.preprocessing import PolynomialFeatures
from sklearn.metrics import mean_squared_error
from sklearn.linear_model import LinearRegression
from math import sqrt
def po
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
data=pd.DataFrame(
{"input":
[0.001,0.015,0.066,0.151,0.266,0.402,0.45,0.499,0.598,0.646,0.738,0.782,0.86,0.894,0.92
我正在使用sklearn线性和多项式特征来拟合数据集。代码如下所示。我使用散点绘制这些点,但它们似乎与预测值不一致。不确定我错过了什么。我尝试将度数值从1更改为20,但没有效果。 import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
DEGREE = 5
X = np.array([276237,276617, 276997, 2773
我使用sklearn运行了这段多项式回归代码,但我的图解与我所期望的不一样。正如你所看到的,,我没有得到一条平滑的线,但是它是从一个点跳到另一个点。根据我的理解,我必须排序X,但是当我这样做的时候,我得到的只是一个带直线的空图。
import operator
import numpy as np
from sklearn.cluster import KMeans
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import make_pipeline
from sklearn.preproces
尝试进行多项式回归,但在拟合模型时遇到了一些问题。获取
ValueError: Found input variables with inconsistent numbers of samples: [1040, 260]
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
x = BTCdata.iloc[:, [1, 2, 4, 5]]
y = BTCd