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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Tradeoff between Prediction Interval Accuracy & Mean Squared Error
My goal is to quantify the prediction uncertainty in a model regressing climate covariates against GDP. I start with a model with temperature as a third degree polynomial, country fixed effects ($\...
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Why does the square term get omitted in Gradient derivation of parameter θ-th
I get it that my question may sound a bit sophisticated or overwhelming, but it's pretty straightforward when you read the image below.
As you can see, the square ^2 completely dissipates, despite ...
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Is Mean Square Prediction Error acceptable to use if predicted values are continuous but actual observed values are discrete?
I would like to compare the predictive power of 2 models. The models are meant to model count data, so the actual observed values are discrete. However both models are designed such that they output ...
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Standard error of RMSE and differences in RMSE
I have a set of models $M = \{1, ..., m, ..., K\}$, and for each I am calculating RMSE on out-of-sample data as standard:
\begin{equation}
\mathrm{RMSE_{m}} = \sqrt{\frac{1}{N} \sum_{i=1}^{N} (\...
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How to improve a model with little dataset? [duplicate]
I have a dataset that has 20 features and 65 samples. I did data scaling. I also did feature selection in different ways. But this is the result.
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Is there any test I can apply to the data to tell whether the adaptive LASSO or the LASSO is likely to perform better in prediction?
Is there a. test I can perform on a sample that will tell me if coefficients estimated using the LASSO, the adaptive LASSO, or the relaxed adaptive LASSO are likely to give better (in the mean squared ...
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MSE of VAR impulse responses in R
I am using the vars library in R. How do I calculate the MSE of the impulse responses I generate with the irf function? The <...
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Optimal estimate under altered MSE loss function
Suppose I am interested in estimating $\theta \in \mathbb{R}$ and I observe a noisy data point $\tilde{\theta}=\theta + N(0,\sigma^2)$ where $\sigma^2$ is known. I am interested in constructing an ...
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Determining an optimal level of aggregation that balances accuracy and granularity
I am looking for ideas for aggregating prediction outcomes in a way that maximizes the number of classes while minimizing classification error.
As a motivating example, say I'm working on a prediction ...
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using MSE loss paired with F-score in a classification model
for a video summarization project i use the features of each frame as input to predict if some of these frames are included in the summary or not.
one of the famous implementations i found had treated ...
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results of a regression predictor
I have a neural network trained to predict values from timeseries.
the target (which is hopefully to be predicted by NN) is always in range 0.0~1.0, and has these statistic features:
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Sample Size for Adaptive Lasso
Be gentle, I'm learning here. I have a fairly simple adaptive lasso regression that I'm trying to test for a minimum sample size. I used cross-validated mean squared error as the "score" of ...
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Lasso regression test MSE lower than train MSE
Im currently using Lasso to build a predictive model for numeric variable .
Before scaling the features I split the data for train test and validation . I have a feature named 'year' and i wanted the ...
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Mean Squared Error for point estimation
I am attempting to understand Mean Squared Error when evaluating point estimators for particular parameters of interest. The book we are reading for class states the following:
The mean squared error (...
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Compare Root Mean Square Values
I'm trying to compare a regression neural network to a commonly used equation. I have an 80:20 split for my training:test, and I get the root mean square error on the test set from the neural network ...
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