MSE stands for mean-squared error. It's a measurement of an empirical loss in certain mathematical models, especially regression models.
Questions tagged [mse]
44 questions
6
votes
1 answer
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 binary_crossentropy (BCE) and not mse ?
According to keras example, the input to the…
user3668129
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4
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2 answers
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 removing those rows and upon @Ben Reiniger's advice, I used LinearRegression and the…
RAbraham
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3
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1 answer
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 fitted with the Mean Squares Error (MSE) as the…
thereandhere1
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3
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1 answer
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 are far lesser.
The random forest regressor…
ml_learner
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3
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2 answers
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 reason.
SKB
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2
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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 other projects these metrics being used.
Now I have a…
Vasilii Naumushkin
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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 using MinMaxSclaer(), and the model is trained with…
MADFROST
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vote
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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 following case:
Lets look at an output layer with 10…
EB97
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1
vote
1 answer
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 shape, i tried below but gave me a
ValueError: Data…
ammar
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vote
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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 an mse of around 0.02 .
Now this mse is valid…
eetiaro
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1
vote
2 answers
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 is around 60. E[D] is another n-dimensional…
Jiaming Na
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1
vote
0 answers
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 the network for relevant weight.
Amalka
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1
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1 answer
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 am wondering for what the $\sum_{i=1}^{n}$ stands.
Do I sum over the loss of all…
Lupos
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1
vote
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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:
from numpy import array
from tensorflow.keras.models import Sequential
from…
T.Sokh
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1 answer
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 in training set, $\theta$ is the actual parameter in the training set and $\theta_m^{-}$…
Fatemeh Asgarinejad
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