(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): $$y = \beta_1 x_{1} + \beta_2 x_{2} + \alpha$$ In this case, I would interpret the absolute contribution of $x_1$ to $y$ to be $\beta_1 x_{1}$, and the relative contribution of $x_1$ to $y$ to be: $$\frac{\beta_1 x_{1}}{y}$$ (assuming everything is positive) Relative contributions from a log-log …
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 of data values unnecessary in logistic regression?
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: $[0,1]$, The range of odds values: $[0,\infty]$, The range of log-odds values: $[-\infty,+\infty]$ Probability & odds lie in a restricted range, while log-odds doesn't. When values lie in a restricted range, the correlation between variables falls. Following is an example where correlation between 'Hours spent …
$\log(\text{odds}) = \text{logit}(P)=ln \big({{P}\over{1-P}}\big)$ $ln\big({{P}\over{1-P}}\big)=\beta_0+\beta_1x$ Consider this example: $0.7=\beta_o+\beta_1(x)+\beta_2(y)+\beta_3(z)$ How can this expression be interpreted?
The range of probability values: $[0,1]$, The range of odds values: $[0,\infty]$, The range of log odds values: $[-\infty,+\infty]$ We use log of odds instead of odds and probability in logistic regression as data can be modeled better when values lie in a non-restricted range. What exactly is the issue with values that lie in a restricted range?
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 the document). Please refer to Wikipedia. It does not include the number of total terms in a document. For example, say, document 1 has 10 words in total and one of them is "happy". Using the …
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!
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 regressions like logarithmic or polynomial regression. a) Is it generally possible to make polynomial regression like in CNN where XGBoost approximates the data by generating n-polynomial function? b) If a) is generally not possible, would it be possible …
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 r2_score from sklearn.base import BaseEstimator from sklearn.linear_model import RANSACRegressor from scipy.optimize import OptimizeWarning from scipy.optimize import curve_fit class LogarithmicRegression(BaseEstimator): def __init__(self, log_base=np.log): self.__log_base = log_base def __log_expr(self, x, a, b, c): with warnings.catch_warnings(): warnings.simplefilter("ignore", RuntimeWarning) return a * self.__log_base(x+c) + …
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 Excel solver but ran into issues where one of the X variables became 0 in the process and gave undefined results.
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 normalize this? Should I make B flat before? Do I put a limit before the max in B and consider all points above like the max? I read you carefully. Thanks in advance.
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 available? Thanks