Questions tagged [sigmoid-curve]
A sigmoid function is a mathematical function having a characteristic "S"-shaped curve or sigmoid curve. Often, sigmoid function refers to a special case of the logistic function. It is closely related to the logistic regression.
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Should I Use Regularization in Univariate Logistic Regression for Diagnostic Methods Comparison?
I am comparing two diagnostic methods, Method 1 and Method 2, where Method 2 is considered the gold standard. I am using Method 1 to predict the Method 2 using logistic regression. My dataset contains ...
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Logistic regression for experimental inference?
I'm curious if logistic regression is appropriate when the task is inferring a treatment effect on proportions (ex: success rate) when covariates are present and some imbalance across treatment and ...
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Python Fitted Sigmoid extreme values barely being used
I was initially dealing with huge sets of couple of values. I used a custom heuristic to compute a score from each couple of values and turn the set into an array of values. I sorted it and assigned ...
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Why GLM fit is different from direct fit using logistic function [closed]
I found in my data that a direct fit using the logistic function gives a different and better (R^2) fit than GLM fit using binomial distribution with logit link function. I was naively expecting the ...
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simplified form for CDF of inverse Gaussian function, as function of the drift parameter
I am thinking about modelling the probability of an event as a waiting time for a Brownian motion with drift to pass a certain barrier. Then consider what the expression for that probability looks ...
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Making linear to logistic regression with sigmoid function - why is a transformation of predicted y needed?
I noticed that one can run a linear regression for binary outcomes and get the same predictions as from a logistic regression after using a sigmoid function. That is what I awaited. But the surprising ...
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Understanding the Logistic regression formula
Logistic regression aims at transforming the linear regression formula and fitting the s curve or logistic function to a particular dataset in order to calculate the probability of a categorical ...
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Logistic Regression - Guesstimating betas
I am beginning an exploration on the topic, and was given the task of 'guesstimating betas 0 and 1' within 90% accuracy.
I tried many silly things, like using the data and then using google sheets to ...
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Seeking advice. Comparing two sigmoideal curves
Probably a very stupid stat question, but I am a bit lost and unsure of what to do! I have conducted a study testing whether two types of stimuli are processed differently. I have presented the two ...
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Logistic function with different asymptotic slopes
The asymptotic slope of both sides of a logistic/sigmoid function is zero. What would be the equation of a logistic function with a different asymptotic slope on each side? I'm trying to model data ...
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Example where the initial random state of a logistic regression matters?
I am looking into how random initialisation of a model would impact final results after tuning. This is a well known problem for deep learning (NN or gbdt), notably with random initialisation and ...
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What is a visual representation of an ordinal logistic fitted model
Naturally, the above image is a graphical representation of a sigmoid function fit for a logistic regression
I'm trying to understand the equivalent of an ordered logit. Below is an image that, to the ...
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Does linear separability with gamma margin guarantee convergence of perceptron algorithm?
I am studying perceptron for the first time. I came across the assumption from online resources that if the data is linearly separable with gamma margin then the perceptron algorithm will converge. Is ...
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Is there a way to stretch sigmoid function output
I have an array of values and a value that lies outside of array's max value:
arr = [10, 15, 20, 30]
value = 150
and I want to make that value less of an outlier, ...
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Influence of big values in hidden layers on the output value of the output layer
In the lab on Tensorflow classification, we are trying to predict wether a coffe is well roasted or badly. A 1 is good roasted and 0 is bad. The architecture is:
Now we try to visualize the trained ...
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