logistic-regression

问题 Can anyone explain how to interpret coefficientMatrix , interceptVector , Confusion matrix of a multinomial logistic regression . According to Spark documentation: Multiclass classification is supported via multinomial logistic (softmax) regression. In multinomial logistic regression, the algorithm produces K sets of coefficients, or a matrix of dimension K×J where K is the number of outcome classes and J is the number of features. If the algorithm is fit with an intercept term then a length

问题 Can anyone explain how to interpret coefficientMatrix , interceptVector , Confusion matrix of a multinomial logistic regression . According to Spark documentation: Multiclass classification is supported via multinomial logistic (softmax) regression. In multinomial logistic regression, the algorithm produces K sets of coefficients, or a matrix of dimension K×J where K is the number of outcome classes and J is the number of features. If the algorithm is fit with an intercept term then a length

问题 I have to predict the type of program a student is in based on other attributes. prog is a categorical variable indicating what type of program a student is in: “General” (1), “Academic” (2), or “Vocational” (3) Ses is a categorical variable indicating someone’s socioeconomic class: “Low” (1), “Middle” (2), and “High” (3) read , write , math , science is their scores on different tests honors Whether they have enrolled or not csv file in image format; import pandas as pd; import numpy as np;

问题 I am willing to perform a logistic regression for my dataset. I use: glm.fit=glm(direccion~Profit, data=datos, family=binomial) Minute ecopet TASA10 direccion Minute cl1 Day Profit 1 571 2160 5 1 571 51.85 2015-02-20 -0.03 2 572 2160 5 1 572 51.92 2015-02-20 0.04 3 573 2160 5 1 573 51.84 2015-02-20 -0.04 4 574 2160 5 1 574 51.77 2015-02-20 -0.11 5 575 2160 10 1 575 51.69 2015-02-20 -0.19 6 576 2165 5 1 576 51.69 2015-02-20 -0.16 7 577 2165 -5 0 577 51.64 2015-02-20 -0.28 8 578 2165 -10 0 578

问题 In analysis of categorical data, we often use logistic regression to estimate relationships between binomial outcomes and one or more covariates. I understand this is a type of generalized linear model (GLM). In R, this is implemented with the glm function using the argument family=binomial . On the other hand, in categorical data analysis are multinomial models. Are these not GLMs? And can't they be estimated in R using the glm function? (In this post for Multinomial Logistic Regression. The

问题 My dataset size is 42542 x 14 and I am trying to build different models like logistic regression, KNN, RF, Decision trees and compare the accuracies. I get a high accuracy but low ROC AUC for every model. The data has about 85% samples with target variable = 1 and 15% with target variable 0. I tried taking samples in order to handle this imbalance, but it still gives the same results. Coeffs for glm are as follow: glm(formula = loan_status ~ ., family = "binomial", data = lc_train) Deviance

问题 I am trying to do best subset selection on the wine dataset, and then I want to get the test error rate using 10 fold CV. The code I used is - cost1 <- function(good, pi=0) mean(abs(good-pi) > 0.5) res.best.logistic <- bestglm(Xy = winedata, family = binomial, # binomial family for logistic IC = "AIC", # Information criteria method = "exhaustive") res.best.logistic$BestModels best.cv.err<- cv.glm(winedata,res.best.logistic$BestModel,cost1, K=10) However, this gives the error - Error in

问题 I use the multinom() function from the nnet package to run the multinomial logistic regression in R. The nnet package does not include p-value calculation and t-statistic calculation. I found a way to calculate the p-values using the two tailed z-test from this page. To give one example of calculating a test statistic for a multinom logit (not really a t-stat, but an equivalent) I calculate the Wald's statistic: mm<-multinom(Empst ~ Agegroup + Marst + Education + State, data = temp,weight

问题 I use the multinom() function from the nnet package to run the multinomial logistic regression in R. The nnet package does not include p-value calculation and t-statistic calculation. I found a way to calculate the p-values using the two tailed z-test from this page. To give one example of calculating a test statistic for a multinom logit (not really a t-stat, but an equivalent) I calculate the Wald's statistic: mm<-multinom(Empst ~ Agegroup + Marst + Education + State, data = temp,weight

问题 I'm wondering how can I get odds ratio from a fitted logistic regression models in python statsmodels. >>> import statsmodels.api as sm >>> import numpy as np >>> X = np.random.normal(0, 1, (100, 3)) >>> y = np.random.choice([0, 1], 100) >>> res = sm.Logit(y, X).fit() Optimization terminated successfully. Current function value: 0.683158 Iterations 4 >>> res.summary() <class 'statsmodels.iolib.summary.Summary'> """ Logit Regression Results =====================================================