logistic-regression

问题 I'm trying to understand the difference between RidgeClassifier and LogisticRegression in sklearn.linear_model . I couldn't find it in the documentation. I think I understand quite well what the LogisticRegression does.It computes the coefficients and intercept to minimise half of sum of squares of the coefficients + C times the binary cross-entropy loss , where C is the regularisation parameter. I checked against a naive implementation from scratch, and results coincide. Results of

问题 I'm trying to understand the difference between RidgeClassifier and LogisticRegression in sklearn.linear_model . I couldn't find it in the documentation. I think I understand quite well what the LogisticRegression does.It computes the coefficients and intercept to minimise half of sum of squares of the coefficients + C times the binary cross-entropy loss , where C is the regularisation parameter. I checked against a naive implementation from scratch, and results coincide. Results of

问题 I found the statsmodels implementation of the anova testing for linear models to be very useful (http://www.statsmodels.org/dev/generated/statsmodels.stats.anova.anova_lm.html#statsmodels.stats.anova.anova_lm) but I was wondering, since it's not present in the library, how one could approach building the equivalent version for the logistic regression part. formulas : from statsmodels.formula.api import ols, logit import statsmodels.api as sm ols(formula_str, data=data_on_which_to_perform

问题 My dataset look like this ID choice_situation Alternative Attr1 Attr2 Attr3 choice ID_1 1 1 0 0 0 0 ID_1 1 2 1 1 0 1 ID_1 2 1 1 1 0 0 ID_1 2 2 1 1 1 1 ID_1 3 1 2 1 0 1 ID_1 3 2 3 1 0 0 ID_2 1 1 3 0 1 1 ID_2 1 2 0 0 0 0 ID_2 2 1 2 1 1 0 ID_2 2 2 2 1 1 1 ID_2 3 1 0 0 0 1 ID_2 3 2 0 0 1 0 ..... Every time I run the code of mlogit function DCE_data<- mlogit.data(data=dataset, choice = "choice", shape = "long", alt.var = "Alternative", id.var = "ID") #ok model<- mlogit(choice ~ Attr1 + Attr2 +

问题 I re-run script that previously worked with no errors about two months ago. I used the haven package to upload an (non-public and proprietary) SPSS dataset and the survey package to analyze complex survey data. Now, however, when I run even a simple logistic regression where both variables are dummies (coded 0 for no and 1 for yes)...something like this... f <- read_sav("~/data.sav") fsd <- svydesign(ids=~1, data=f, weights=~f$weight) model <- svyglm(exclhlth~male,design=fsd,family

问题 Is there a R-package to calculate pseudo R-squared measures for my model? rcompanion neither supports clogit nor bife (due to missing intercept?). Originally that was one question out of a larger context, which I edited to make it more readable. Thanks in advance for your help! 回答1: Relative to question 5: Definitions for pseudo r-squared values based (mostly) on log likelihood values are given by UCLA IDRE. If you are able to extract the log likelihood from the fitted model and null model,

问题 Is there a R-package to calculate pseudo R-squared measures for my model? rcompanion neither supports clogit nor bife (due to missing intercept?). Originally that was one question out of a larger context, which I edited to make it more readable. Thanks in advance for your help! 回答1: Relative to question 5: Definitions for pseudo r-squared values based (mostly) on log likelihood values are given by UCLA IDRE. If you are able to extract the log likelihood from the fitted model and null model,

问题 I'm trying to recreate a plot from An Introduction to Statistical Learning and I'm having trouble figuring out how to calculate the confidence interval for a probability prediction. Specifically, I'm trying to recreate the right-hand panel of this figure (figure 7.1) which is predicting the probability that wage>250 based on a degree 4 polynomial of age with associated 95% confidence intervals. The wage data is here if anyone cares. I can predict and plot the predicted probabilities fine with

问题 I am a complete beginner in machine learning and coding in python, and I have been tasked with coding logistic regression from scratch to understand what happens under the hood. So far I have coded for the hypothesis function, cost function and gradient descent, and then coded for the logistic regression. However on coding for printing the accuracy I get a low output (0.69) which doesnt change with increasing iterations or changing the learning rate. My question is, is there a problem with my

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