mean-square-error

Why calculating MSE in lasso regression gives different outputs?

由 寵の児 提交于 2019-12-22 09:09:13
问题 I am trying to run different regression models on the Prostate cancer data from the lasso2 package. When I use Lasso, I saw two different methods to calculate the mean square error. But they do give me quite different results, so I would want to know if I'm doing anything wrong or if it just means that one method is better than the other ? # Needs the following R packages. library(lasso2) library(glmnet) # Gets the prostate cancer dataset data(Prostate) # Defines the Mean Square Error

Why not to use mean square error for classification problem

由 半城伤御伤魂 提交于 2019-12-11 17:45:49
问题 I am trying to implement a simple binary classification problem using RNN LSTM and still not available to figure out the correct loss function for the network. The issue is, when I use the cross_binary_entophy as loss function, the loss value for training and testing is relatively high as compared to using a mean_square_error function. Upon research, I came across to justifications that binary cross entropy should be used for classification problem and MSE for the regression problem. However,

MSE Cost Function for Training Neural Network

由 旧巷老猫 提交于 2019-12-11 06:14:27
问题 In an online textbook on neural networks and deep learning, the author illustrates neural net basics in terms of minimizing a quadratic cost function which he says is synonymous with mean squared error. Two things have me confused about his function, though (pseudocode below). MSE≡(1/2n)*∑‖y_true-y_pred‖^2 Instead of dividing the sum of squared errors by the number of training examples n why is it instead divided by 2n ? How is this the mean of anything? Why is double bar notation used

How to get mean square error in a quick way using Matlab?

由 試著忘記壹切 提交于 2019-12-10 19:12:48
问题 I don't know whether this is possible or not but let me explain my question Imagine that I have the below array errors=[e1,e2,e3]; Now what I want to calculate is below MSE=1/(array_lenght)*[e1^2+e2^2+e3^2]; I can make this with a loop but I wonder if there is any quick way. 回答1: This finds the mean of the squared errors: MSE = mean(errors.^2) Each element is squared separately, and then the mean of the resulting vector is found. 回答2: sum(errors.^2) / numel(errors) 回答3: Raising powers and

Why calculating MSE in lasso regression gives different outputs?

由 ε祈祈猫儿з 提交于 2019-12-05 17:18:50
I am trying to run different regression models on the Prostate cancer data from the lasso2 package. When I use Lasso, I saw two different methods to calculate the mean square error. But they do give me quite different results, so I would want to know if I'm doing anything wrong or if it just means that one method is better than the other ? # Needs the following R packages. library(lasso2) library(glmnet) # Gets the prostate cancer dataset data(Prostate) # Defines the Mean Square Error function mse = function(x,y) { mean((x-y)^2)} # 75% of the sample size. smp_size = floor(0.75 * nrow(Prostate)

Mean Squared Error in Numpy?

由 爷，独闯天下 提交于 2019-11-28 20:01:42
Is there a method in numpy for calculating the Mean Squared Error between two matrices? I've tried searching but found none. Is it under a different name? If there isn't, how do you overcome this? Do you write it yourself or use a different lib? Saullo G. P. Castro You can use: mse = ((A - B)**2).mean(axis=ax) Or mse = (np.square(A - B)).mean(axis=ax) with ax=0 the average is performed along the row, for each column, returning an array with ax=1 the average is performed along the column, for each row, returning an array with ax=None the average is performed element-wise along the array,

Why is the Cross Entropy method preferred over Mean Squared Error? In what cases does this doesn't hold up? [closed]

由 末鹿安然 提交于 2019-11-28 04:22:23
Although both of the above methods provide better score for better closeness of prediction, still cross-entropy is preferred. Is it in every cases or there are some peculiar scenarios where we prefer cross-entropy over MSE? Cross-entropy is prefered for classification , while mean squared error is one of the best choices for regression . This comes directly from the statement of the problems itself - in classification you work with very particular set of possible output values thus MSE is badly defined (as it does not have this kind of knowledge thus penalizes errors in incompatible way). To

Mean Squared Error in Numpy?

由 无人久伴 提交于 2019-11-27 12:38:35
问题 Is there a method in numpy for calculating the Mean Squared Error between two matrices? I've tried searching but found none. Is it under a different name? If there isn't, how do you overcome this? Do you write it yourself or use a different lib? 回答1: You can use: mse = ((A - B)**2).mean(axis=ax) Or mse = (np.square(A - B)).mean(axis=ax) with ax=0 the average is performed along the row, for each column, returning an array with ax=1 the average is performed along the column, for each row,

Why is the Cross Entropy method preferred over Mean Squared Error? In what cases does this doesn't hold up? [closed]

由 梦想与她 提交于 2019-11-26 22:37:30
问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed last year . Although both of the above methods provide better score for better closeness of prediction, still cross-entropy is preferred. Is it in every cases or there are some peculiar scenarios where we prefer cross-entropy over MSE? 回答1: Cross-entropy is prefered for classification , while

What function defines accuracy in Keras when the loss is mean squared error (MSE)?

由 微笑、不失礼 提交于 2019-11-26 05:29:35
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 - val_acc: 0.3250 Epoch 100/100 1000/1000 [==============================] - 688s 688ms/step - loss: 0
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