python去除离群_用Python去除离群值线性回归

这是我为简单线性回归创建的代码。这是密码，我有几个问题，我正在寻找答案。

如何从X和Y中检测和删除异常值也许一个代码示例会有所帮助？

您对模型部分的培训和评估质量有何看法？

正确的交叉验证？列车试验装置？

如何解释RMSE值？大价值观是好兆头还是坏兆头？在import pandas as pd

import numpy as np

import warnings

import matplotlib.pyplot as plt

import statsmodels.api as sm

from scipy import stats

# Import Excel File

data = pd.read_excel ("C:\Users\AchourAh\Desktop\Simple_Linear_Regression\SP Level Simple Linear Regression\PL32_PMM_03_09_2018_SP_Level.xlsx",'Sheet1') #Import Excel file

# Replace null values of the whole dataset with 0

data1 = data.fillna(0)

print(data1)

# Extraction of the independent and dependent variable

X = data1.iloc[0:len(data1),1].values.reshape(-1, 1) #Extract the column of the COPCOR SP we are going to check its impact

Y = data1.iloc[0:len(data1),2].values.reshape(-1, 1) #Extract the column of the PAUS SP

# Data Splitting to train 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 =0.25,random_state=42)

# Training the model and Evaluation of the Model

from sklearn.linear_model import LinearRegression

from sklearn import model_selection

lm = LinearRegression() #create an lm object of LinearRegression Class

lm.fit(X_train, Y_train) #train our LinearRegression model using the training set of data - dependent and independent variables as parameters. Teaching lm that Y_train values are all corresponding to X_train values.

from sklearn.model_selection import KFold

kf = KFold(n_splits=6, random_state=None)

for train_index, test_index in kf.split(X_train

print("Train:", train_index, "Validation:",test_index)

X_train1, X_test1 = X[train_index], X[test_index]

Y_train1, Y_test1 = Y[train_index], Y[test_index]

results = -1 * model_selection.cross_val_score(lm, X_train1, Y_train1,scoring='neg_mean_squared_error', cv=kf)

print(results)

print(results.mean())

y_pred = lm.predict(X_test)

from sklearn.metrics import mean_squared_error

mse_test = mean_squared_error(Y_test, y_pred)

print(mse_test)

import math

print(math.sqrt(mse_test))

print(math.sqrt(results.mean()))

df = pd.DataFrame({'Actual': [Y_test], 'Predicted': [y_pred]})

print(df)

# Graph of the Training model

plt.scatter(X_train, Y_train, color = 'red')#plots scatter graph of COP COR against PAUS for values in X_train and y_train

plt.plot(X_train, lm.predict(X_train), color = 'blue')#plots the graph of predicted PAUS against COP COR.

plt.title('SP000905974')

plt.xlabel('COP COR Quantity')

plt.ylabel('PAUS Quantity')

plt.show()#Show the graph

# Statistical Analysis of the training set with Statsmodels

X2 = sm.add_constant(X_train) # add a constant to the model

est = sm.OLS(Y_train, X2).fit()

print(est.summary()) # print the results

# Statistical Analysis of the training set with Scikit-Learn

params = np.append(lm.intercept_,lm.coef_)

predictions = lm.predict(X_train)

newX = pd.DataFrame({"Constant":np.ones(len(X_train))}).join(pd.DataFrame (X_train))

MSE = (sum((Y_train-predictions)**2))/(len(newX)-len(newX.columns))

var_b = MSE*(np.linalg.inv(np.dot(newX.T,newX)).diagonal())

sd_b = np.sqrt(var_b)

ts_b = params/ sd_b

p_values =[2*(1-stats.t.cdf(np.abs(i),(len(newX)-1))) for i in ts_b]

sd_b = np.round(sd_b,3)

ts_b = np.round(ts_b,3)

p_values = np.round(p_values,5)

params = np.round(params,4)

myDF1 = pd.DataFrame()

myDF1["Coefficients"],myDF1["Standard Errors"],myDF1["t values"],myDF1["P-values"] = [params,sd_b,ts_b,p_values]

print(myDF1)

我是初学者，如果编码有问题，我也愿意接受其他评论？在