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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Logistic regression : incoherent coefficient values from z tests?

I am working on a customer purchase problem. I have 150 campaigns sent by email, that I denote C0, C1 ... ...
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Is Wikipedia's page on the sigmoid function incorrect?

Is Wikipedia's page on the sigmoid function incorrect? It states that: A common example of a sigmoid function is the logistic function From my knowledge of machine learning, I thought that "the ...
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Is there any difference between a Logistic Regression for inference and for prediction?

When studying Linear Regression, I remember that Multicollinearity is something that impacts inference and not prediction, at least not always. Also, I noted that the assumptions tends to be neglected ...
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Re-parameterization to resolve non-identifiability in this squiggly model (linear combination of logistic functions)?

So my desire here is to be able to capture a variety of temporal dynamics governing the change in value of some feature of interest. I want the model to be able to represent, for example, bounded: ...
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27 views

Implementing logistic regression model in prediction model - use outcome or probability?

My question deals with the principles or way of thinking about implementing a logistic regression model in a larger scale forward prediction. Let's say I have a trained logistic model which can ...
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19 views

Using Sigmoid in Maximum Likelihood Estimates [duplicate]

I have two questions regarding the use of Sigmoid in MLE: Clearly, the Sigmoid Function is not a PDF. But in the MLE of Logistic Regression, we see Sigmoid being used as if it is a PDF. Is my ...
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For prediction problems, why cant we simply use softmax as activation for hidden layers and no activation function for output layer

I am a beginner who is learning how to use neural networks for prediction problems, and almost all tutorials I have found use relu for hidden layer and sigmoid function for output later. My question ...
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Likelihood values from Sigmoid [duplicate]

Repost of Mathemetics StackExchange question. There are multiple doubts of mine associated around this theme: In MLE, we try to find the PDF parameters ($\theta$) which maximise the likelihood of the ...
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2answers
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Why $1/(1+e^{-x}) = e^x/(1+e^x)$

I am currently learning the sigmoid/logistic function and have completely forgotten how the maths behind this equivalence works: $$ \dfrac{1}{1+ e^{-x}} = \dfrac{e^{x}}{1+e^{x}} $$ By this I mean how ...
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sklearn logistic regression converging to unexpected coefficient for a simple case

The case is as follows: Suppose that import numpy as np X = np.array([1, 1, 1]) y = np.array([1, 0, 1]) Then I perform a logistic regression with no intercept to ...
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What value of predictions minimizes the binary cross entropy loss function? Is it 0.5?

I saw some examples of Autoencoders (on images) which use sigmoid as output layer and ...
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Do you do linear regression in logistic regression?

I just studied single & multiple linear regression 2 days ago now I'm reading about logistics regression and I want to implement it from scratch, I just want to know If my understanding about ...
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Binary logistic regression coefficients

Consider the following MWE example. A dataset with only one feature (categorical feature with 4 different categories ['cat', 'dog', 'hamster', 'frog']) + target (overall 10% positive class). After ohe ...
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Calculate future revenue based on a log growth formula similar to exponential growth formula?

The exponential (not continuous) growth formula from high school is: $$A(t)=A^{kt}$$ Where $A$ is the initial amount, $t$ is the number of time periods and $k$ is the shape or growth rate. Looking ...
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When I normalize my X, it changes the curve

I am going between Excel and R for this understand the fitted lines of this experiment. I have X and I have Y. When plotted it give me a curve that looks slightly sigmoidal, but I realize the X goes ...
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Exponential growth curve similar to SSlogis?

I'm able to fit a logistic growth curve, e.g. like this. Example provided in that link: ...
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Decision boundary of logistic Regression and Hypothesis space in R

I am trying to generate a decision boundary of logistic regression. My Training set is 2/3 and the test set is 1/3, I have however tried producing the decision ...
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253 views

Sigmoid vs Softmax Accuracy Difference

I have trained a neural network on DNA sequences data and my training set has exactly the same number of data in both classes. When I select a softmax function at the end, my accuracy remains at 47% ...
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Fitting a sigmoid model to my data and comparing parameters for different factors

I acquired data from 20 subjects for 4 different conditions (2 factors) with 14 different intensity levels, created using a log-space distribution. I would like to model the response for each ...
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How does Keras Evaluate handle Multilabel Sigmoid Problems

As I am working on tuning a model multilabel densely connected model, I am realizing that I don't understand how the model is being evaluated. My current model has 20 potential labels and ends with a ...
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434 views

Logistic growth curve with R nls

I would like to fit a model 'logistic-growth' or 'sigmoid growth' per exercise 'Try It #3' over on this online textbook (almost halfway down the page): \begin{array}{|c|c|c|c|} \hline \text{Year}&...
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R fit a logistic regression of the form $y=\frac{c}{1+ae^{-bx}}$ [duplicate]

I'm working through some textbook exercises on fitting models to data, including exponential, logarithmic and now logistic regression. About a 3rd of the way down this page is the section on logistic ...
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1answer
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Binary classification of each column in an image

I have a problem where I wish to classify each column in an image of a feature being present (1) or absent (0) in each column. The output of the model should be a vector of size of the width of an ...
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What is the extension(or generalisation) of the sigmoid function to non-binary classification? [duplicate]

I understand that one of the main points of using the sigmoid function on responses in binary classification is that we can interpret value outputted as the probability that an instance belongs to 1 ...
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Where does this formula "New odd=(old Odd)*(Exponentiated coefficient)*Change in independent variable)" come from? Logistic Regression

I'm working in a project using the logistic Regression and was reading some books when I saw this formula in the book titled "Multivariate Data Analisys" by Joseph Hair and others. I don't ...
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Sigmoid confidence curves in logistic regression

I have a logistic regression model that predicts the probability of an outcome based on one input variable. I am able to plot the curve just fine and show my group how the probability is predicted to ...
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Logistic probabilities of state variable in a hidden Markov model always has variance of zero

Here is a simplified version of a more complicated problem that I have. Imagine a hidden Markov model where the state is $X_t\sim N(\mu,\sigma^2)$. The observed variable is $Y_t\sim Bin(N, p_t)$ ...
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Measuring how much each data point contributes to the output of a sigmoid function

I am not sure I am asking this on the right place, if not, please redirect me :) So I am dealing with the following problem. I have a set of data points (local shapely values), and I sum them up and ...
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27 views

Fit two logistic regressions for two groups separately and Compare them

I want to fit logistic regression with a binary response, and a continuous independent variable (e.g. Age). There are two groups of data, and I want to compare the models between the two groups to see ...
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20 views

Dependency of the activation function in gradient descent calculations

I am working on linear classification script that uses gradient descent to do a binary classification of an object based on two features. I'm working with just a neuron. The output of the neuron uses ...
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1answer
68 views

Rounding output of sigmoid for binary linear classifier

I am working on a linear classifier with expected output to be 1 for class A belonging and 0 for class B belonging. The output, in some occasions is nearly 0 (...
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1answer
49 views

Adjustment factor in logistic growth model of facebook-prophet

In the paper it emphasizes: "When the rate k is adjusted, the offset parameter m must also be adjusted to connect the endpoints of the segments. The correct adjustment at changepoint j is easily ...
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Why SSgompertz does not work on similar data sets?

I have data that follows a sigmodial shape and the Gompertz function seem to make sense. I wanted to use SSgompertz in nls to find the parameters, but I get sometimes errors. So, I wanted to learn ...
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What is $\int_{c}^{\infty}\Phi(a+bx) \phi( x ) dx$ for $c\in\mathbb{R}$

It is straightforward to show $$\int_{-\infty}^{\infty}\Phi(a+bx) \phi( x ) dx = \Phi\left(\frac{a}{\sqrt{1+b^2}}\right)$$ but what value does $$\int_{c}^{\infty}\Phi(a+bx) \phi( x ) dx$$ have for $c&...
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Calculate probability of outcome of a medical procedure

I have data on medical procedures completed at hospitals in major U.S hospitals. Each medical procedure is assigned a code, for example: Kidney Transplant is X6571. I define the success criteria and ...
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39 views

How to model a sigmoid function? [closed]

I have a variable where Y ~ sigmoid(X). Y ranges between 0 and 1. Y, however, is not a probability, so I don't know if I can just get a logistic regression. What I do want is, for a change in X, ...
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How is an 'ogival function' defined?

Reading on a paper on factor analysis and measurement invariance I find the description of some functions as 'ogival' functions. In Google I find it referenced mostly in papers from the '70s and '80s....
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24 views

Backpropagation With Sigmoid Output Function Question

I am deriving a Weight update for a simple toy network with a Sigmoid Output Layer. I need some help double checking my math to make sure I did it correctly. I am using Cross-Entropy Loss as my Loss ...
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27 views

Strange situation with softmax activation vs sigmoid activation in the last layer of a binary classification problem?

I am building a neural network as a binary classifier with one output neuron at the last output layer. I deliberately balancing out my train label so that the number label corresponding to the binary ...
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23 views

Conceptual explanation of taking derivative of sigmoid function in neural network

In a neural network, we have a bunch of inputs and corresponding weights + a bias which are represented by a multivariable equation. Now we squash this whole equation with a sigmoid function. How ...
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281 views

Why is learning slower for a sigmoid activation function in a neural network?

Andrew Ng in one of his deep learning course videos says that the sigmoid function acts as a slow learner in a neural network. My intuition is that the sigmoid as an activation function contributes ...
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1answer
204 views

Statistical comparison of numerous nonlinear model parameters

I have 84 data sets (n=3) corresponding to 28 conditions (sample composition and temperature) and have fit my data set to the following nonlinear model using MATLAB nonlinear curve fitting: $$y = \...
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63 views

the loss suddenly was stuck to a very large value after having several epoch

When I changed the final activation layer in the same model from softmax to sigmoid in order to multilabel classification, the loss would suddenly get stuck at a very big value after several epoch. I ...
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1answer
794 views

Neural Network: Matlab uses different activation functions for different layers - why?

I have trained on matlab an Artificial Neural Network with one input layer, one hidden layer and one output layer (my output is values between zero and one, which I turn into 0 or 1 according to a ...
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151 views

Logistic regression risk prediction model - poor calibration but good discrimination

I am trying to create risk prediction model in R. I am new to logistic regression risk prediction analysis. I obtained reliability curve using ...
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1answer
188 views

Decile Analysis for model comparison

I am working on a simple classification problem (bank marketing response data) and trying various evaluation methods for multiple models to compare and understand the output. And I was trying to do a ...
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933 views

Normalizing continuous features using sigmoid function

Can you use the sigmoid function to normalize continuous features that have no theoretical maximum value but tend to cluster around [-1, 1]? Although using the sigmoid function would be a non-linear ...
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166 views

Can binary cross entropy loss be used for non-binary data? [duplicate]

I am following this keras tutorial to construct a convolutional MNIST autoencoder. The decoder has a sigmoid activation function and the entire autoencoder is trained with binary_crossentropy loss. ...