我寫了一個(gè)多元多項(xiàng)式回歸的代碼，我使用了來自sklearn的多項(xiàng)式特征和轉(zhuǎn)換函數(shù)。有可能進(jìn)行多元對(duì)數(shù)回歸嗎？

sklearn是否有某種對(duì)數(shù)變換，就像它對(duì)多項(xiàng)式特征的那樣？

如何在python中編寫多元對(duì)數(shù)回歸？在

這是我的多元多項(xiàng)式特征代碼：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))

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