Linked Questions
51 questions linked to/from Why does minimizing the MAE lead to forecasting the median and not the mean?
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Can someone give the intuition behind Mean Absolute Error and the Median? [duplicate]
I do not understand the intuition behind why the median is the best estimate if we are going to judge prediction accuracy using the Mean Absolute Error. Let's say you have a random variable $X$ and ...
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Quantile loss 50th is MAE, is it? [duplicate]
I'm not sure the above sentence is true, but I read it here, here and here that quantile loss function percentile 0.5 is MAE (mean absolute error). Is it true (yes or no)? And How?
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What are the shortcomings of the Mean Absolute Percentage Error (MAPE)?
The Mean Absolute Percentage Error (mape) is a common accuracy or error measure for time series or other predictions,
$$ \text{MAPE} = \frac{100}{n}\sum_{t=1}^n\frac{|A_t-F_t|}{A_t}\%,$$
where $A_t$ ...
- 131k
23
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(Why) Is absolute loss not a proper scoring rule?
Brier score is a proper scoring rule and is, at least in the binary classification case, square loss.
$$Brier(y,\hat{y}) = \frac{1}{N} \sum_{i=1}^N\big\vert y_i -\hat{y}_i\big\vert^2$$
Apparently this ...
- 67.2k
8
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Median of a set with even number of elements
Consider the following sample of numbers:
9,18,11,14,15,17,10,69,11,13
Since there are 10 elements, the median of the set would be (13+14)/2 = 13.5
However, is it wrong to say that median is one of 13 ...
Vedant B
11
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3
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What measure can I use to select a number from a dataset which future values will most likely be closest to?
Say I have a dataset that shows me the number of times per day someone has used a mobile app that I have developed. And that dataset (after sorting) looks like this:
...
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6
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Best loss function for nonlinear regression
I have some nonlinear nonnormal data that I am trying to analyze. The data has been normalized to -1 to 1 and detrended with polynomials of an order of 3. I'm trying to determine if there is a special ...
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Linear regression minimising MAD in sklearn
The standard sklearn linear regression class finds an approximated linear relationship between variate and covariates that minimises the mean squared error (MSE). Specifically, let $N$ be the number ...
2
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3
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Can we represent a population by its mean? [closed]
Can we represent a population by its mean?
Is the answer simply "yes, unless there are outliers or the data is skewed"?
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8
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2
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Which metric to use for estimating accuracy of a climate model?
Let's assume I have 3 different climate models for a specific region that project the temperature. I also have the observations of that region for the same time frame(the real temperatures). My ...
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Low hanging fruits for a simple NN
I have only the basic understanding of Neural Networks (NN). Recently, I encountered a scenario at my company where a team was using linear regression (LR) to forecast an important continuous ...
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How to optimize MAPE in regression algorithms
I have a regression task where the label is varying from about 0.001 to 1000. One of the feature called group, for example, group A corresponding label from 0-0.1 and group G corresponding label from ...
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6
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How to incentivise AI to make risky predictions
I'm trying to build a weather forecasting AI. I have a dataset that contains the peak temperatures for each day. I have trained it with MSE as the loss function and it has worked fairly well. I do ...
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Higher RMSE lower MAPE
I have a time series model that forecast next K days. For example when I forecast next 50 days my MAPE is 20.3% and RMSE is 2943 and when I forecast next 200 days is the MAPE is 10.25 % but RMSE is ...
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Acceptable limit for MASE
What are good sign of fit from result of forecast::accuracy.
How to interpret
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