Questions tagged [logarithmic]
The logarithmic tag has no usage guidance.
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Why does the application of the logarithmic function improve the outcome of Random forests?
I have a Random forest model that tries to predict what kind of a useful activity a machine is doing based on its power readings. There are 5 features in a single reading.
There are two main types of ...
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name of log(n+1) plot
I am trying to plot a distribution of positive integers which contains a lot of variance. I opted to use the log of the y-values but that causes issues due to the inclusion of zeros. I though of ...
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Understanding log odds equation with multiple variables
"If we take the antilog of the regression coefficient associated with obesity, exp(0.415) = 1.52 we get the odds ratio adjusted for age. The odds of developing CVD are 1.52 times higher among ...
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How can a log transformation decrease performance?
I'm working on a Demand Forecasting project, I have a lot of 0 (75% of the database)
I got a highly right skewed target (5.5).
So I decided to log transform my target: target = log(target + 1)
When I ...
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Get result from log transformed variable
I can't find some documentation.
I had right-skewed target (sale price) variable and also some skewed features at the same way. I did log transformation and fit the regression model and it doing well. ...
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Effect of log odds on skewed data
Does taking the log of odds bring linearity between the odds of the dependent variable & the independent variables by removing skewness in the data? Is this one reason why we use log of odds in ...
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Log odds understanding
Here is my understanding of one reason why we prefer log odds over odds & probability. Please let me know if I got it right.
Reasons why we choose log-odds-
The range of probability values: $[0,1]$...
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Interpretation of Log Odds in Logistic Regression
$\log(\text{odds}) = \text{logit}(P)=ln \big({{P}\over{1-P}}\big)$
$ln\big({{P}\over{1-P}}\big)=\beta_0+\beta_1x$
Consider this example: $0.7=\beta_o+\beta_1(x)+\beta_2(y)+\beta_3(z)$
How can this ...
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How to justify logarithmically scaled frequency for tf in tf-idf?
I am studying tf-idf (term frequency - inverse document frequency). The original logic for tf was straightforward: count of term t / number of total terms in the document.
However, I came across the ...
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Impact of log transformation and Normalisation in the context of EDA and ML
Is data normalisation an alternative for log transformation? I understand that both helps us to normalisation helps me to make my distribution gaussian.
Thanks in advance for your help!
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XGBoost non-linear regression
Is it possible to use XGBoost regressor to do non-linear regressions?
I know of the objectives linear and logistic.
The ...
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RANSAC and R2, why the r2 score is negative?
I was experimenting with curve_fit, RANSAC and stuff trying to learn the basics and there is one thing I don´t understand.
Why is R2 score negative here?
...
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How to maximize a log linear regression equation satisfying a constraint?
I have a log linear equation of the form $y = w_1(\log{X1}) + w_2(\log{X2}) + ... + w_n(\log{Xn})$.
How can I find the value of X's that maximize the value of y subject to a constraint $(X_1+X_2+...+...
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Can absolute or relative contributions from X be calculated for a multiplicative model? $\log{ y}$ ~ $\log {x_1} + \log{x_2}$
(How) can absolute or relative contributions be calculated for a multiplicative (log-log) model?
Relative contributions from a linear (additive) model
E.g., there are 3 contributors to $y$ (given by ...
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Dealing with zeros when plotting log-scaled data [closed]
I have a non-negative variable and I'd like to plot it, log-scaled
I'm trying to understand how to deal with 0-values. One naive idea I had in mind is just to add <...
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Normalizing variables with logarithmic shape
A simple model with two variables [A,B] to train, let's say, a logistic regression or any other classification model:
A: Flat distribution from 0 to 100.
B: A logarithmic distribution from 0
to a few ...
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