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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Predict actual result after model trained with MinMaxScaler LinearRegression

I'm sorry I'm new to modeling and still learning, I was doing the modeling on the House Pricing dataset. My target is to get the mse result and predict with the input variable I have done the modeling,...
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30 views

Proof for MSE = Var + Bias2

I am trying to prove the equality of $$\rm MSE=Var+Bias^2$$ but obviously I got something wrong as they don't equal in my calculation: So here is the example. I use monte carlo to estimate this ...
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MAE vs MSE for linear regression

Several articles says that MAE is robust to outliers but MSE is not and MSE can hamper the model if errors are too huge. My question is that MSE and MAE both are error matrices,our priority is to just ...
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Do I need to rescale input labels before training (label values between 20..51)?

I'm trying to build model for this datatset (Age prediction): The input image has the shape: 3, 128, 128 and the predicted labels (ages) range between 20 to 51. I ...
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Trying to implement a loss function read from a journal-article in python

Computer science undergrad here. I am trying to understand Eqn 12 from this paper so that I can implement it in python code. In this paper, the NN model takes a blurred image as input and outputs a ...
1 vote
1 answer
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how to calculate loss function?

i hope you are doing well , i want to ask a question regarding loss function in a neural network i know that the loss function is calculated for each data point in the training set , and then the ...
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Why does log-transforming the target have a huge impact on MSE value?

I am doing linear regression using the Boston Housing data set, and the effect of applying $\log(y)$ has a huge impact on the MSE. Failing to do it gives MSE=34.94 ...
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Batch Size influences R2 score a lot, but not MSE (much)

If I train a model following a random search, (and in general for this problem I am working on), a big batch size seems to control R2 score where bs=200 or more, say, roughly, gives R2 scores of 0.95 ...
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What causes explosion in MSE when training?

I (probably) well overfitted/overtrained a model. But I was just curious as to what might cause this type of behaviour. I carried on training (Epoch 1/50 is not the first epoch of training this model)....
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weighted mse - weights as function of time

I am predicting timeseries data using LSTM (in tensorflow). Currently I am using MSE as my metric of choice. I would like to create my own custom Weighted MSE metric, such that the weights are a ...
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Increasing (negative) R2 coincident with decreasing (positive) MSE during hyper parameter optimisation

I have a densely connected NN and I'm running a hyper parameter optimisation for multi-target output. During hyper parameter optimisation training, each epoch KerasTuner focuses on val_loss. During ...
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How to extract MSEP or RMSEP from lassoCV?

I'm doing lasso and ridge regression in R with the package chemometrics. With ridgeCV it is easy to extract the SEP and MSEP values by ...
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1 vote
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Can't understand an MSE loss function in a paper

I'm reading a paper published in nips 2021. There's a part in it that is confusing: This loss term is the mean squared error of the normalized feature vectors and can be written as what follows: ...
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Appropriate loss function and metrics for regression task with mixed outputs

I'm trying to train an EfficientNet-based Keras model that takes an image as input and returns two numeric values as output. Here's the model: ...
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Find $a, b, c$ minimizing MSE

Suppose you are given a "dummy" classifier. It looks like this: $$ y(x) = \begin{cases} a \text{ if } x >= c \\ b \text{ else } \end{cases} $$ Given some data set $\{(y_1, x_1), \dots (...
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Keras Custom loss Penalize more when actual and prediction are on opposite sides of Zero

I'm training a model to predict percentage change in prices. Both MSE and RMSE are giving me up to 99% accuracy but when I check how often both actual and prediction are pointing in the same direction ...
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Why the error between the measured data and model data is not minimizing in Python?

I want to fit the non-linear experimental data with the model function by estimating some parameters in the function. The model function I have is: ...
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6 votes
1 answer
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Why autoencoders use binary_crossentropy loss and not mean squared error?

Keras autoencoders examples: (https://blog.keras.io/building-autoencoders-in-keras.html) use binary_crossentropy (BCE) as loss function. Why they use ...
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770 views

Why training loss is decreasing down too fast?

I have a dataset of video sequences, I have trained them, and calculated the training loss using mean square error, but my training loss is decreasing down very fast. Like 0.06-0.02. Is it just fine ...
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what n represents in the MSE loss function?

Neural Network Loss Function - Mean Square Error: questions about what 'n' signifies I can't understand how the answers in this question answered the question. please help me to understand the ...
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What is bad, good and excellent metric score for time series model?

I have created a couple of models for my master project and I used several metrics for evaluation. I used MSE, MAE, MAPE, RMSE not because I really learned about them a lot, because I saw in many ...
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1 answer
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neg_mean_squared_error in cross_val_score [closed]

The string "mean_squared_error" appears to be deprecated in cross_val_score now, and it's saying to use ...
1 vote
1 answer
2k views

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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3 answers
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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 ...
2 votes
1 answer
1k views

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 ...
0 votes
2 answers
427 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 ...
1 vote
1 answer
998 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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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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1 answer
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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 ...
1 vote
2 answers
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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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1 answer
158 views

PyTorch MultiLayer Perceptron Classification Size of Features vs Labels Wrong

I am getting the following error: ...
4 votes
2 answers
1k views

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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1 answer
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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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1 vote
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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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1 answer
6k views

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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3 votes
1 answer
395 views

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 ...
3 votes
2 answers
2k 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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1 vote
1 answer
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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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1 vote
1 answer
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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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1 answer
310 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^...
1 vote
1 answer
240 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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65 views

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 ...