Questions tagged [mse]

MSE stands for Mean Squared Error. It is a measure of the performance of an estimate or prediction, equal to the mean squared difference between the observed values and the estimated / predicted values.

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Why are the "Loss Functions" being Optimized in most Statistical/Machine Learning Problems usually "Quadratic"? [duplicate]

Why are the "Loss Functions" being Optimized in most Statistical/Machine Learning Problems usually "Quadratic"? Using very basic logic, in statistics/machine learning we are trying ...
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How is the decay rate in exponential smoothing optimized?

For the sake of simplicity, I just want to focus on single/level exponential smoothing. When alpha, the decay rate, is near 1, the most recent observation has the highest weight and influence of ...
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What can cause test-set MARE to be lower when trained on MSE?

I am currently working on a CNN 1D model for regression. The ultimate goal is to minimize the mean absolute percentage (relative) error, so-called MAPE or MARE. However, the MARE on the test set is ...
34 views

What is the difference between least squares method and mean squared method in calculating the error?

I think I am a little bit confused between the LSE (Least Squared Error) and the MSE (Mean Squared Error). So how can these two methods differ in calculating the error of the linear regression model? ...
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How to calculate RMSE for two regression lines

I'm struggling on how can I calculate the RMSE for two regressions. Consider the following scenario: I have two linear regressions, and Id like to calculate the joint RMSE for this model. Any hint on ...
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Drastically high MAPE error but MAE is normal [duplicate]

I am training an autoencoder which takes sampled time series sensor data in range [-1024,1024] (0 values is possible). I use mean_squared loss and Adam optimizer. During the training MAE decreases and ...
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Difference between minimizing RMSE or MSE in non linear least squares?

I am working with R with this code from the book "Bootstrap Methods: With Applications in R" by Gerhard Dikta and Marsel Scheer: ...
37 views

What is the theoretical justification for alternatives to MSE minimisation?

I'm trying to wrap my head around the connection between statistical regression and its probability theoretical justification. In many books on statistics/machine learning, one is introduced to the ...
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Cross Entropy for sigmoid/tanh regression

My neural network has a tanh activation function for the output layer. It would be no problem to change this to sigmoid. The labels are values in the same range. By this I mean that the target value ...
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How to prove Mean Squarred Error (MSE)

I would like to prove this equation of Mean Squared Error (MSE): m is the number of training instances. X is a m × n matrix containing all the feature values (excluding labels) of all instances in ...
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What optimisation method is used in ordered logistic regression?

I am using the polr function in R to create an ordered logistic model and am curious to know what its optimisation method is? It seems to perform better than other models I have tested it against when ...
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Can Mean Square Error cause underfitting?

Mean Square Error (MSE) is used in Regression problems to compute the error in prediction. Large errors have a large influence on the MSE, and small errors have almost negligible influence on the ...
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Evaluating Supervised Model Performance Against a Baseline

My question is regarding how I can interpret the performance of a supervised ML task relative to a baseline estimator. I have run a supervised ML as a regression, and used K-fold CV to evaluate ...
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