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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Meaning of mean squared error in multistep prediction
In multistep prediction with LSTM(keras), say we had this kind of result:
target = [[1,2,3] ,[4,5,6] ]
predictions = [[1.1,2.2,3.3] , [4.4,5.5,6.6]]
When we choose mean_squared_error as the loss ...
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Proper metric for measuring the similarity between two images
I want to calculate the similarity between these two images:
and
These are brain topography maps and colors inside the circles represent the area being activated while watching TV. Thus I am looking ...
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How to lower MSE using polynomial regression?
I have a training dataset with the positions (x and y) of three objects and their velocities at a time t. Then I have a test dataset with the initial positions and a time step x.
The goal is to ...
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what's the difference between using 1/2n and 1/n in cost function in Linear Regression? [duplicate]
Sometimes people use 1/2n in cost function, but we know that another name for cost function is Mean Squared Error. But for MSE, 1/n is justifiable than 1/2n, so is there any term that we call when we ...
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How do you appropriately measure the real mean squared error of a box cox transformed linear regression model?
My understanding is that it can make sense to transform the outcomes of a linear regression model to make them more normally distributed. That's because it could 1) help me find more linear ...
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Why COST FUNCTION AND MSE IS CALLED THE SAME?
Why are the cost function and mean squared errors called the same thing? WHEN THE COST FUNCTION IS 1/2M AND THE MSE IS 1/N. AND M=N
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How to select the validation loss value in this model to be compared with other models?
I'm training an LSTM model. I'm confusing about the validation loss of the model. Which value better represents the validation loss of the model? Is it the last value I obtain in the floowing loop, or ...
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ValueError: Found input variables with inconsistent numbers of samples: [120, 30]
I practice XGBClassifier() to predict the target in iris dataset.
here is the code:
...
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Plateaus training loss but validation loss decreases
I am training a fully convolutional regressor, with mobilenet as its backbone. I have already overcome a massive overfitting problem by augmenting the data. However, the training loss seems to be ...
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Hello! I am create linear model in python, and have question. It's bad score or good score?
please, take me info, this bad or good?
I am real don't understand....
i'm know, also when
Mean Absolute Deviation (MAD): In [0,∞), the smaller the better
Root Mean Squared Error (RMSE): In [0,∞), the ...
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Relation between MSE and variance of data
I am having a hard time understanding how to compare results of MSE and variance of data to eachother. I understand that MSE is used to calculate how far off data points are from a prediction, say you ...
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Predict actual result after model trained with MinMaxScaler LinearRegression
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, I'm doing the modeling with scaling the data ...
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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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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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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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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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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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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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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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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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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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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 ...
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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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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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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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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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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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PyTorch MultiLayer Perceptron Classification Size of Features vs Labels Wrong
I am getting the following error:
...
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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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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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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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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:
$$L(a,b) = {\sum_{i=1}^n (y^...
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Math behind, $MSE = bias^2 + variance$
Based on the deeplearningbook:
$$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 ...
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