logistic-regression

问题 I have a logistic regression model trying to predict one of two classes: A or B. My model's accuracy when predicting A is ~85%. Model's accuracy when predicting B is ~50%. Prediction of B is not important however prediction of A is very important. My goal is to maximize the accuracy when predicting A. Is there any way to adjust the default decision threshold when determining the class? classifier = LogisticRegression(penalty = 'l2',solver = 'saga', multi_class = 'ovr') classifier.fit(np

问题 I'm trying to make the following graphic below in ggplot2. See that I am adjusting a logistic regression model under the Bayesian approach using the stan package. data: https://drive.google.com/file/d/1NNA6DFYFSVkwuL92RYe58E_Z99BXl7HE/view?usp=sharing library(dplyr) library(rstanarm) library(ggplot2) dados<- read.table("dados.csv", header = T, sep=";", dec = ",") dados$periodo <- as.factor(dados$periodo) dados <- dados %>% mutate(proporcao = (dados$resposta)/60) dados <- dados %>% mutate

问题 I want to calculate two confusion matrix for my logistic regression using my training data and my testing data: logitMod <- glm(LoanStatus_B ~ ., data=train, family=binomial(link="logit")) i set the threshold of predicted probability at 0.5: confusionMatrix(table(predict(logitMod, type="response") >= 0.5, train$LoanStatus_B == 1)) And the the code below works well for my training set. However, when i use the test set: confusionMatrix(table(predict(logitMod, type="response") >= 0.5, test

问题 I am trying to plot a decision region (based on the output of a logistic regression) with matplotlib contourf funtion. The code I am using: subplot.contourf(x2, y2, P, cmap=cmap_light, alpha = 0.8) where x2 and y2 are two 2D matrices generated via numpy meshgrids. P is computed using P = clf.predict(numpy.c_[x2.ravel(), y2.ravel()]) P = P.reshape(x2.shape) Each element of P is a boolean value based on the output of the logistic regresssion. The rendered plot looks like this My question is how

问题 I'm trying to estimate a logistic unit fixed effects model for panel data using R. My dependent variable is binary and measured daily over two years for 13 locations. The goal of this model is to predict the value of y for a particular day and location based on x. zero <- seq(from=0, to=1, by=1) ids = dplyr::data_frame(location=seq(from=1, to=13, by=1)) dates = dplyr::data_frame(date = seq(as.Date("2015-01-01"), as.Date("2016-12-31"), by="days")) data = merge(dates, ids) data$y <- sample(zero

问题 I'm new to using statsmodels to do statistical analyses. I'm getting expected answers most of the time but there are some things I don't quite understand about the way that statsmodels defines endog (dependant) variables for logistic regression when entered as strings. An example Pandas dataframe to illustrate the issue can be defined as shown below. The yN, yA and yA2 columns represent different ways to define an endog variable: yN is a binary variable coded 0, 1; yA is a binary variable

问题 I'm trying to estimate child work based on a lagged variable on children's school aspirations. I'm deciding whether I should use glm or clogit to run my models (need fixed effect logits). When I run my glm, my coefficients are very different from my clogit. model1 <- glm(chldwork~lag_aspgrade_binned+age+as.factor(childid), data=finaletdtlag, family='binomial') GLM Output: Call: glm(formula = chldwork ~ lag_aspgrade_binned + age + as.factor(childid), family = "binomial", data = finaletdtlag)

问题 I have been using sjplot to create a combined table. This creates a HTML table. I would like to make a table that can be exported to word. I have reviewed this post which discusses copy and pasting into word, but this alters the formatting of the columns and lines. Output several regression tables into multiple pages of a Word document in R n1 <- glm(N ~ Age_2 , data = n_data, family = "binomial") g1 <- glm(G ~ Age_2 , data = g1_data, family = "binomial") ga1 <- glm(G_1 ~ Age_2 , data = ga1

问题 I have been using sjplot to create a combined table. This creates a HTML table. I would like to make a table that can be exported to word. I have reviewed this post which discusses copy and pasting into word, but this alters the formatting of the columns and lines. Output several regression tables into multiple pages of a Word document in R n1 <- glm(N ~ Age_2 , data = n_data, family = "binomial") g1 <- glm(G ~ Age_2 , data = g1_data, family = "binomial") ga1 <- glm(G_1 ~ Age_2 , data = ga1

问题 I have been using sjplot to create a combined table. This creates a HTML table. I would like to make a table that can be exported to word. I have reviewed this post which discusses copy and pasting into word, but this alters the formatting of the columns and lines. Output several regression tables into multiple pages of a Word document in R n1 <- glm(N ~ Age_2 , data = n_data, family = "binomial") g1 <- glm(G ~ Age_2 , data = g1_data, family = "binomial") ga1 <- glm(G_1 ~ Age_2 , data = ga1