regression

Neural Networks are mostly used to classify. So, the activation of a neuron in the output layer indicates the class of whatever you are classifying. Is it possible (and correct) to design a NN to get 3D coordinates? This is, three output neurons with values in ranges, for example [-1000.0, 1000.0], each one. Yes. You can use a neural network to perform linear regression , and more complicated types of regression, where the output layer has multiple nodes that can be interpreted as a 3-D coordinate (or a much higher-dimensional tuple). To achieve this in TensorFlow, you would create a final

We are using a programming language that does not have a linear regression function in it. We have already implemented a single variable linear equation: y = Ax + B and have simply calculated the A and B coefficents from the data using a solution similar to this Stack Overflow answer . I know this problem gets geometrically harder as variables are added, but for our purposes, we only need to add one more: z = Ax + By + C Does anyone have the closed form equations, or code in any language that can solve for A, B and C given an array of x, y, and z's? so you have three linear equations k = aX1 +

Can anyone explain to me how I can apply non linear regression to this equation t find out K using the matlab command window. I = 10^-9(exp(38.68V/k)-1). Screenshot of Equation I have data values as follows: Voltage := [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]: Current:= [0, 0, 0, 0, 0, 0, 0, 0.07, 0.92, 12.02, 158.29]: Screenshot of Equation [NEW]: Now I used FminSearch as an alternative another and another error message appeared. Matrix dimensions must agree. Error in @(k)sum((I(:)-Imodel(V(:),k)).^2) Error in fminsearch (line 189) fv(:,1) = funfcn(x,varargin{:}); I used this

I am doing a regression using Generalized Linear Model.I am caught offguard using the crossVal function. My implementation so far; x = 'Some dataset, containing the input and the output' X = x(:,1:7); Y = x(:,8); cvpart = cvpartition(Y,'holdout',0.3); Xtrain = X(training(cvpart),:); Ytrain = Y(training(cvpart),:); Xtest = X(test(cvpart),:); Ytest = Y(test(cvpart),:); mdl = GeneralizedLinearModel.fit(Xtrain,Ytrain,'linear','distr','poisson'); Ypred = predict(mdl,Xtest); res = (Ypred - Ytest); RMSE_test = sqrt(mean(res.^2)); The code below is for calculating cross validation for mulitple

I am investigating Knn regression methods and later Kernel Smoothing. I wish to demonstrate these methods using plots in R. I have generated a data set using the following code: x = runif(100,0,pi) e = rnorm(100,0,0.1) y = sin(x)+e I have been trying to follow a description of how to use "knn.reg" in 9.2 here: https://daviddalpiaz.github.io/r4sl/k-nearest-neighbors.html#regression grid2=data.frame(x) knn10 = FNN::knn.reg(train = x, test = grid2, y = y, k = 10) My predicted values seem reasonable to me but when I try to plot a line with them on top of my x~y plot I don't get what I'm hoping for

According to what I have understood, linear regression predicts the outcome which can have continuous values, whereas logistic regression predicts outcome which is discrete. It seems to me that logistic regression is similar to a classification problem. So, why is it called regression ? There is also a related question: What is the difference between linear regression and logistic regression? There is a strict link between linear regression and logistic regression. With linear regression you're looking for the k i parameters: h = k 0 + Σ k i ˙ X i = K t ˙ X With logistic regression you

Note this is not a question about multiple regression, it is a question about doing simple (single-variable) regression multiple times in Python/NumPy (2.7). I have two m x n arrays x and y . The rows correspond to each other, and each pair is the set of (x,y) points for a measurement. That is, plt.plot(x.T, y.T, '.') would plot each of m datasets/measurements. I'm wondering what the best way to perform the m linear regressions is. Currently I loop over the rows and use scipy.stats.linregress() . (Assume I don't want solutions based on doing linear algebra with the matrices but instead want to