regression

问题 How is Accuracy defined when the loss function is mean square error? Is it mean absolute percentage error? The model I use has output activation linear and is compiled with loss= mean_squared_error model.add(Dense(1)) model.add(Activation('linear')) # number model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy']) and the output looks like this: Epoch 99/100 1000/1000 [==============================] - 687s 687ms/step - loss: 0.0463 - acc: 0.9689 - val_loss: 3.7303 -

问题 How is Accuracy defined when the loss function is mean square error? Is it mean absolute percentage error? The model I use has output activation linear and is compiled with loss= mean_squared_error model.add(Dense(1)) model.add(Activation('linear')) # number model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy']) and the output looks like this: Epoch 99/100 1000/1000 [==============================] - 687s 687ms/step - loss: 0.0463 - acc: 0.9689 - val_loss: 3.7303 -

问题 How is Accuracy defined when the loss function is mean square error? Is it mean absolute percentage error? The model I use has output activation linear and is compiled with loss= mean_squared_error model.add(Dense(1)) model.add(Activation('linear')) # number model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy']) and the output looks like this: Epoch 99/100 1000/1000 [==============================] - 687s 687ms/step - loss: 0.0463 - acc: 0.9689 - val_loss: 3.7303 -

问题 This is an example data frame, where PRE5_id1,POST5_id1, PRE5_id2, POST5_id2 are the variables that I would like to get. I am looking for a lead and lag value which will have five values of 1 in the years before natural death (PRE5) and 5 years after the year of natural death (POST5). I am not sure how to stay within the group of country when creating these PRE and POST variables, in which case the PRE and POST variables go to +5 and -5 only within the same country. I am planning to do a

问题 This is an example data frame, where PRE5_id1,POST5_id1, PRE5_id2, POST5_id2 are the variables that I would like to get. I am looking for a lead and lag value which will have five values of 1 in the years before natural death (PRE5) and 5 years after the year of natural death (POST5). I am not sure how to stay within the group of country when creating these PRE and POST variables, in which case the PRE and POST variables go to +5 and -5 only within the same country. I am planning to do a

问题 I am currently doing an assignment for my data analysis course at uni. I manage to do the first two parts without many problems (EDA and text processing). I now need to do this: Build a regression model that will predict the rating score of each product based on attributes which correspond to some very common words used in the reviews (selection of how many words is left to you as a decision). So, for each product you will have a long(ish) vector of attributes based on how many times each

问题 I am having an error whenever I try to use gam as a method in caret.train function. fit<- train(P~log(DR)+log(L2M)+s(TSM)+s(TH)+s(II),data=training,method="gam") Error: $ operator is invalid for atomic vectors Here is one of the warnings: In eval(expr, envir, enclos) : model fit failed for Resample16: select=FALSE, method=GCV.Cp Why is this happening? When I just use gam everything is fine, this only happens with caret package. dput(head(training)) output: structure(list(TT = c(1.810376, 0

问题 I need to fit two curves(which both should belong to cubic functions) into a set of points with JuMP. I've done fitting one curve, but I'm struggling at fitting 2 curves into same dataset. I thought that if I can distribute points to curves - so if each point can only be used once - I can do it like below, but it didn't work. (I know that I can use much more complicated things, I want to keep it simple.) This is a part of my current code: # cubicFunc is a two dimensional array which accepts

问题 I recently tried to plot a regression pane in RStudio using the plotly library and read this post: Add Regression Plane to 3d Scatter Plot in Plotly I followed the exact same procedure and ended up with a regression plane, which is obviously not correct: EDIT: I followed the proposal in the first answer and my result looks like this: Here is my code, I commented every step: sm is the data.frame I used library(reshape2); sm <- read.delim("Supermodel.dat", header = TRUE); x1 <- sm$age x2 <- sm

问题 Suppose I have a data with a factor and response variable. My questions: How linear regression and mixed effect models work with the factor variables? If I have a separate model for each level of the factor variable (m3 and m4) , how does that differ with models m1 and m2 ? Which one is the best model/approach? As an example I use Orthodont data in nlme package. library(nlme) data = Orthodont data2 <- subset(data, Sex=="Male") data3 <- subset(data, Sex=="Female") m1 <- lm (distance ~ age +