regression

Hi I'm learning Statsmodel and can't figure out the difference between : and * (interaction terms) for formulas in StatsModels OLS regression. Could you please give me a hint to figure this out? Thank you! The documentation: http://statsmodels.sourceforge.net/devel/example_formulas.html Yaron ":" will give a regression without the level itself. just the interaction you have mentioned. "*" will give a regression with the level itself + the interaction you have mentioned. for example a . GLMmodel = glm("y ~ a: b" , data = df) you'll have only one independent variable which is the results of "a"

I realise that there are posts on the topic of B-Splines on this board but those have actually made me more confused so I thought someone might be able to help me. I have simulated data for x-values ranging from 0 to 1. I'd like to fit to my data a cubic spline ( degree = 3 ) with knots at 0, 0.1, 0.2, ... , 0.9, 1. I'd also like to use the B-Spline basis and OLS for parameter estimation (I'm not looking for penalised splines). I think I need the bs function from the spline package but I'm not quite sure and I also don't know what exactly to feed it. I'd also like to plot the resulting

I want to use Gaussian Processes to solve a regression task. My data is as follow : each X vector has a length of 37, and each Y vector has a length of 8. I'm using the sklearn package in Python but trying to use gaussian processes leads to an Exception : from sklearn import gaussian_process print "x :", x__ print "y :", y__ gp = gaussian_process.GaussianProcess(theta0=1e-2, thetaL=1e-4, thetaU=1e-1) gp.fit(x__, y__) x : [[ 136. 137. 137. 132. 130. 130. 132. 133. 134. 135. 135. 134. 134. 1139. 1019. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 70. 24. 55. 0. 9. 0. 0.] [ 136. 137. 137. 132. 130

How does plot.lm() determine what points are outliers (that is, what points to label) for residual vs fitted plot? The only thing I found in the documentation is this: Details sub.caption—by default the function call—is shown as a subtitle (under the x-axis title) on each plot when plots are on separate pages, or as a subtitle in the outer margin (if any) when there are multiple plots per page. The ‘Scale-Location’ plot, also called ‘Spread-Location’ or ‘S-L’ plot, takes the square root of the absolute residuals in order to diminish skewness (sqrt(|E|)) is much less skewed than | E | for

I have created a simple neural network (Python, Theano) to estimate a persons age based on their spending history from a selection of different stores. Unfortunately, it is not particularly accurate. The accuracy might be hurt by the fact that the network has no knowledge of ordinality. For the network there is no relationship between the age classifications. It is currently selecting the age with the highest probability from the softmax output layer. I have considered changing the output classification to an average of the weighted probability for each age. E.g Given age probabilities: (Age

I am using a CNN for a regression task. I use Tensorflow and the optimizer is Adam. The network seems to converge perfectly fine till one point where the loss suddenly increases along with the validation error. Here are the loss plots of the labels and the weights separated (Optimizer is run on the sum of them) I use l2 loss for weight regularization and also for the labels. I apply some randomness on the training data. I am currently trying RSMProp to see if the behavior changes but it takes at least 8h to reproduce the error. I would like to understand how this can happen. Hope you can help