Questions tagged [logarithmic]
18 questions
5
votes
1 answer
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 linear objective works very good with the gblinear booster.
This made me wonder if it is possible to use XGBoost for non-linear…
Harrys Kavan
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2
votes
1 answer
Standard Scaling After Log Transformation
I have a quick question about whether or not to standardize features after a log transformation.
I have one feature that is heavily skewed and requires the log transformation, for the other features I'm applying the standard scaler.
My question is,…
atn291
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2
votes
1 answer
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 logistic regression?
If yes, then is log transformation…
Apoorva
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1
vote
0 answers
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 $(X_1+X_2+...+X_n \le t)$ in python or excel?
I tried using…
my_cse lab
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1
vote
2 answers
Dealing with zeros when plotting log-scaled data
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 1 to all values (or some very low number greater than 1)
What other options are…
Elimination
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1
vote
0 answers
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 thousands.
What would be the proper way to…
miguelbadajoz
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1
vote
1 answer
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 plotting log10(n+1), but it seems a bit janky.
Is this…
iHnR
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1
vote
1 answer
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 obese persons as compared to non obese persons,…
Apoorva
- 337
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- 19
1
vote
1 answer
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. But I can't understand how I can return to…
Olha
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1
vote
1 answer
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 log scaled frequency: log(1 + count of term t in…
Fred Chang
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1
vote
1 answer
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?
import numpy as np
import warnings
import matplotlib.pyplot as plt
from sklearn.metrics import…
mavetek
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- 4
0
votes
1 answer
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 the three additive terms):
$$y = \beta_1 x_{1} +…
Ben
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0
votes
0 answers
How to determine outliers based on a regression logarithmic-scaled?
I'm facing a problem were I'd like to detect outliers from a data collection. The goal is to be able to identify outliers from a variable Y based on its relation with the variable X.
To do that, I did:
A plot in logarithmic scale
A regression…
Arya
- 1
0
votes
1 answer
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 activities: Main (A set of useful activities.…
Nht_e0
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0
votes
2 answers
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 train my models (linear regression or LGBM,…
Dummy01
- 1