regression

I've made a multilayer LSTM model that uses regression to predict next frame's values of the data. The model finishes after 20 epochs. I then get some predictions and compare them to my ground truth values. As you can see them in the picture above, predictions converge to a constant value. I don't know why this happens. Here is my model so far: from keras.models import Sequential from keras.layers.core import Dense, Activation, Dropout from keras.layers import LSTM, BatchNormalization from tensorflow.python.keras.initializers import RandomUniform init = RandomUniform(minval=-0.05, maxval= 0.05

I have a problem at hand which I'd think is fairly common amongst groups were R is being adopted for Analytics in place of SAS. Users would like to obtain results for logistic regression in R that they have become accustomed to in SAS. Towards this end, I was able to propose the Design package in R which contains many functions to extract the various metrics that SAS reports. If you have suggestions pertaining to other packages, or sample code that replicates some of the SAS outputs for logistic regression, I would be glad to hear of them. Some of the requirements are: Stepwise variable

I want to do a regression of y~x (just 1 dependent and 1 independent variable) but I have heteroskedasticity. The variability of y increases as x increases. To deal with it, I would like to use weighted least squares through the "gls()" function in R. But I have to admit that I don't understand how to use it. I have to apply a variance function to the "weights" argument of the gls function. But I don't which one to choose and how to use it. Here's an example of taking care of poisson count like data where the variation will be proportional to the mean (which it sounds like you have). fit = lm

I've fitted a logistic regression model that predicts the a binary outcome vs from mpg ( mtcars dataset). The plot is shown below. How can I determine the mpg value for any particular vs value? For example, I'm interested in finding out what the mpg value is when the probability of vs is 0.50. Appreciate any help anyone can provide! model <- glm(vs ~ mpg, data = mtcars, family = binomial) ggplot(mtcars, aes(mpg, vs)) + geom_point() + stat_smooth(method = "glm", method.args = list(family = "binomial"), se = FALSE) The easiest way to calculate predicted values from your model is with the predict

it's known that when the number of variables (p) is larger than the number of samples (n) the least square estimator is not defined. In sklearn I receive this values: In [30]: lm = LinearRegression().fit(xx,y_train) In [31]: lm.coef_ Out[31]: array([[ 0.20092363, -0.14378298, -0.33504391, ..., -0.40695124, 0.08619906, -0.08108713]]) In [32]: xx.shape Out[32]: (1097, 3419) Call [30] should return an error. How does sklearn work when p>n like in this case? EDIT: It seems that the matrix is filled with some values if n > m: # need to extend b matrix as it will be filled with # a larger solution