regression

问题 I have a really weird problem I want to explain to you. I am not sure if this is a topic for SO but I hope it will be in the end. My general problem task is depth estimation, i.e. I have an image as input and its corresponding ground_truth (depth image). Then I have my net (which should be considered as black box) and my last layers. First of all depth estimation is rather a regression task than a classification task. Therefore I decided to use a EuclideanLoss layer where my num_output of my

问题 I'd like to know what method is best suited for predicting event occurrences. For example, given a set of data from 5 years of malaria infection occurrences and several other factors that affect the occurrences, I'd like to predict the next five years for malaria infection occurrences. What I thought of doing was to derive a kind of occurrence factor using fuzzy logic rules, and then average the occurrences with the occurrence factor to get the first predicted occurrence, and then average all

问题 I have this matlab code for regression with one indepenpent variable, but what if I have two independent variables(x1 and x2)? How should I modify this code of polynomial regression? x = linspace(0,10,200)'; % independent variable y = x + 1.5*sin(x) + randn(size(x,1),1); % dependent variable A = [x.^0, x]; % construct a matrix of permutations w = (A'*A)\(A'*y); % solve the normal equation y2 = A*w; % restore the dependent variable r = y-y1; % find the vector of regression residual plot(x, [y

问题 I need Java library that performs gradient descent (preferred) or other multidimensional continuous optimization methods. Specifically, I need it to find logit regression coefficients. Does anyone knows such a lib? 回答1: I suggest you try Weka - it's a package for Data Mining and Machine Learning. For example, logistic regression is in this class. 来源： https://stackoverflow.com/questions/8296197/java-library-for-multidimensionial-continuous-optimization

问题 In R, the default method when using a factor variable in regression is to use contrasts. I.E we set a reference class, and then the results are reported as (factor) vs. reference. For example, if we had 3 groups, and set 1 to be the reference group, then the results will be (2 vs 1) and (3 vs 1). But we don't get to see 3 vs 2. I know that you can get this by rerunning the regression and re-leveling with 2 as the reference class. But is there any way to get all comparisons in one line of code

问题 In R, the default method when using a factor variable in regression is to use contrasts. I.E we set a reference class, and then the results are reported as (factor) vs. reference. For example, if we had 3 groups, and set 1 to be the reference group, then the results will be (2 vs 1) and (3 vs 1). But we don't get to see 3 vs 2. I know that you can get this by rerunning the regression and re-leveling with 2 as the reference class. But is there any way to get all comparisons in one line of code

问题 I am new to Machine-learning and trying to understand the correct and suitable evaluation for RandomForestRegressor. I have mentioned below Regression metrics and understood these concepts. I am not sure that Which metrics I can use the for RandomForestRegressor's evaluation. Can I use r2_score all the time after prediction ? I am using sklearn packages. Regression metrics See the Regression metrics section of the user guide for further details. metrics.explained_variance_score(y_true, y_pred

问题 I am getting very high RMSE and MAE for MLPRegressor , ForestRegression and Linear regression with only input variables scaled (30,000+) however when i scale target values aswell i get RMSE (0.2) , i will like to know if that is acceptable thing to do. Secondly is it normal to have better R squared values for Test (ie. 0.98 and 0.85 for train) Thank You 回答1: It is actually a common practice to scale target values in many cases. For example a highly skewed target may give better results if it

问题 I am working on a dataset where my target variable CLASS has three categorical values. Now When I apply Ordinal Logistic Regression and run the polr command. Its showing this error "attempt to find suitable starting values failed". I think my target variable is not ordered. Can anybody tell me how to arrange Sv of ordered values? model <- polr(Class~., data= training, Hess = TRUE) Error in polr(Class ~ ., data = training, Hess = TRUE) : attempt to find suitable starting values failed In

问题 I've obtained Schoenfeld residuals for testing PH for Fine & Gray's Cox regression model in presence of competing events with cmprsk. Here's the code: fg<-crr( fu_m, event, diabetes, failcode=1, cencode=0, na.action=na.omit, gtol=1e-06, maxiter=10, variance=TRUE) fg$res fg$uft par(mfrow = c(1,1), mar = c(4.5,4,2,1)) for(j in 1:ncol(fg$res)) fg$u scatter.smooth(fg$uftime, fg$res[,j], main = names(fg$diabetes)[j], xlab = "Failure time", ylab = "Schoenfeld residuals") Here's the output: > fg$res