我编写了一个多元多项式回归的代码,我使用了 sklearn 中的多项式特征和变换函数。是否可以进行多元对数回归? sklearn 是否有某种对数变换,就像多项式特征一样? 如何在 python 中编写多元对数回归?
这是我的多元多项式特征的代码:
import numpy as np
import pandas as pd
import math
import xlrd
from sklearn import linear_model
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import PolynomialFeatures
#Reading data from excel
data = pd.read_excel("DataSet.xls").round(2)
data_size = data.shape[0]
#print("Number of data:",data_size,"\n",data.head())
def polynomial_prediction_of_future_strength(input_data, cement, blast_fur_slug,fly_ash,
water, superpl, coarse_aggr, fine_aggr, days):
variables = prediction_accuracy(input_data)[2]
results = prediction_accuracy(input_data)[3]
n = results.shape[0]
results = results.values.reshape(n,1) #reshaping the values so that variables and results have the same shape
#transforming the data into polynomial function
Poly_Regression = PolynomialFeatures(degree=2)
poly_variables = Poly_Regression.fit_transform(variables)
#accuracy of prediction(splitting the dataset on train and test)
poly_var_train, poly_var_test, res_train, res_test = train_test_split(poly_variables, results, test_size = 0.3, random_state = 4)
input_values = [cement, blast_fur_slug, fly_ash, water, superpl, coarse_aggr, fine_aggr, days]
input_values = Poly_Regression.transform([input_values]) #transforming the data for prediction in polynomial function
regression = linear_model.LinearRegression() #making the linear model
model = regression.fit(poly_var_train, res_train) #fitting polynomial data to the model
predicted_strength = regression.predict(input_values) #strength prediction
predicted_strength = round(predicted_strength[0,0], 2)
score = model.score(poly_var_test, res_test) #accuracy prediction
score = round(score*100, 2)
accuracy_info = "Accuracy of concrete class prediction: " + str(score) + " %\n"
prediction_info = "Prediction of future concrete class after "+ str(days)+" days: "+ str(predicted_strength)
info = "\n" + accuracy_info + prediction_info
return info
#print(polynomial_prediction_of_future_strength(data, 214.9 , 53.8, 121.9, 155.6, 9.6, 1014.3, 780.6, 7))
最佳答案
如果您想拟合特征的对数,一种选择是 Box-Cox 变换,然后是 OLS,您可以使用 PowerTransformer 在 sklearn 中应用它。 https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PowerTransformer.html#sklearn.preprocessing.PowerTransformer
关于python - 如何用Python和sklearn编写多元对数回归?,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/54949969/