Questions tagged [mse]

MSE stands for mean-squared error. It's a measurement of an empirical loss in certain mathematical models, especially regression models.

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input shape of keras Sequential model

i am new to neural networks using keras, i have the following train samples input shape (150528, 1235) and output shape is (154457, 1235) where 1235 is the training examples, how to put the input ...
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MSE relevance as a metric when errors < 1

I'm trying to build my first models for regression after taking MOOCs on deep learning. I'm currently working on a dataset whose labels are between 0 and 2. Again, this is a regression task, not ...
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Match between objective function and evaluation metric

Does the objective function for model fitting and the evaluation metric for model validation need to be identical throughout the hyperparameter search process? For example, can a XGBoost model be ...
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148 views

MSE loss in VAE reduces only KL divergence

I am trying to reproduce the two stage VAE from this repository in TF2 with Keras to learn MNIST digits. Unfortunately I am experiencing behavior that I can not explain to myself: As far as I ...
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2answers
170 views

Regression performance with Feature Selection

I would like to ask you a theoretical question. In my project I am trying to get a better performance from my regression model by feature selection methods, especially with CatBoost feature ...
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1answer
292 views

What would be the mse (mean squared error) of my scaled dataset on the original scale?

I build an LSTM model on a standardized dataset using sklearn's MinMaxScaler. All values of the dataset are between 0 and 1. Features and target variables were standardized between 0 and 1. I achieve ...
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Consecutive Feature Selection-CV and Model Selection-CV

I want to ask a question about general workflow of algorithm development. I want to include a "feature selection with Random Forest" step into my workflow but I have doubts about data leakage. It is ...
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28 views

When optimizing the MSE, the correlation between prediction and target increases?

After optimizing the MSE (mean squared error) in a regression task, how is the change in Pearson correlation coeficient between target vector and the prediction? Is any behaviour possible? Or is sure ...
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52 views

I am getting very minimal mse values and not sure if it is correct?

Below is the linear regression model I fitted and not sure if I am doing the right way as I am getting neat to 99% accuracy Fitting Simple Linear Regression to the Training set ...
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54 views

Finding a vector that minimize the MSE of its linear combination

I have been doing a COVID-19 related project. Here is the question: N = vector of daily new infected cases D = vector of daily deaths E[D] = estimation of daily deaths N is a n-dimensional vector, n ...
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Extremely high MSE/MAE for Ridge Regression(sklearn) when the label is directly calculated from the features

Edit: Removing TransformedTargetRegressor and adding more info as requested. Edit2: There were 18K rows where the relation did not hold. I'm sorry :(. After ...
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Understanding MSE,R2 scores wrt different scaling methods and non intutive results

EDIT: Added Code and updated the metric values as my code changed If I have the Income Statements of all the companies currently trading in the US, I would like to predict the gross profit. I was ...
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Which MSE (total or individual) back-propagate for multi out regression neural network

When we have multi output regression neural network, we can calculate total MSE and individual MSE per output. How this MSE should back-propagate ? Shouldn't we back-propagate individual MSE through ...
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How to Minimize mean square error using Python

I want to minimise mean square error function to find best alpha value (decay rate) for my model. Here is the description of my model: ...
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Confusion about the MSE ERROR

I created a Random Forest regressor model and calculated my own error. I also want to calculate MAE, MSE and RMSE to compare my results to similar use cases. I am confused by the results as the values ...
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732 views

Reason for generally using RMSE instead of MSE in Linear Regression

In linear regression, why we generally use RMSE instead of MSE? The rationale I know is that it's easy to minimize the error in RMSE instead of MSE by Gradient Descent, but I need to know the exact ...
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34 views

Carlification of the MSE loss sum symbol

So I have a question regarding the MSE loss on the application of a Neural Network. Loss function: $\text{MSE} = \frac{1}{2} \sum_{i=1}^{n} (Y_i - \hat{Y_i}) ^ 2$ I ...
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why is MSE of prediction way different from loss over batches

I am new to machine learning so forgive me if i ask stupid question. I have a time series data and i split it into training and test set. This is my code: ...
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67 views

Coefficients of Linear regression for minimizing MSE

(I asked this in mathematics site, but nobody responded, it seems the whole problem is more related to data science than math.) In a regression problem, loss function is: $$L(a,b) = {\sum_{i=1}^n (y^...
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117 views

Math behind, MSE = bias^2 + variance

Based on the deeplearningbook: $$MSE = E[(\theta_m^{-} - \theta)^2]$$ $$equals$$ $$Bias(\theta_m^{-})^2 + Var(\theta_m^{-})$$ where m is the number of samples ...
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A substitute formula for MSE

I don't understand where this formula for Mean Squared Error is coming from. How do we arrive at: $$MSE = \frac{1}{m}||y' - y||_2^2$$ from: $$MSE = \frac{1}{m}\cdot\sum_i(y'_{i} - y_{i})^2$$ (The ...