Questions tagged [logistic-regression]

Refers generally to statistical procedures that utilize the logistic function, most commonly various forms of logistic regression

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Loss Function for Probability Regression

I am trying to predict a probability with a neural network, but having trouble figuring out which loss function is best. Cross entropy was my first thought, but other resources always talk about it in ...
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For Logistic regression, why is that particular logistic function chosen as opposed to other logistic functions?

The logistic function used in logistic regression is: $\frac{e^{B_{0} + B_{1}x}}{1 + e^{B_{0} + B_{1}x}}$. Why is this particular one used?
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Two steps optimization of a credit card limit

I have a problem similar to what is on the title but not the same. The problem on the title allows me to explain the dynamics of my need. I have to determine what the optimal value is for a variable ...
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3 votes
1 answer
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Issues with self-implemented logistic regression

I am trying to self-implement a logistic regression algorithm to do some self-learning but I am having a bit of trouble with achieving similar accuracy to the logistic regression of sklearn. Here is ...
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4 answers
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ML: Classification Model Comparison

Given is a dataset that I need to use for a classification and I want to compare the performance of different classification models. Let's assume, I want to look at logistic regression (with ...
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3 votes
1 answer
356 views

Error while plotting Logistic Regression Classification

I was trying to plot by using the following code ...
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2 votes
2 answers
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Why do we don't write units with MAE or RSME for regression problem ? If I wish to write the units when how do I identify the units for them?

I have referred many research paper but no one is talking about the units of the metrics. Do MAE , RMSE etc have some units ?
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2 votes
1 answer
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What is the best Classification Method alternative to Nominal Logistic Regression, if your Response and all Predictor variables are Categorical?

Hy, I need help in choosing the best classification method. My response variable is nominal with "4" categories and five predictor variables, two of them are nominal and three are binary. ...
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2 votes
0 answers
660 views

Binary classification with imbalanced dataset, about lightgbm output probability distribution

I trained a binary classifier for an imbalanced dataset. I did two experiments: lightgbm classifier, boosting_type='gbdt', objective='cross_entropy', SMOTE upsample After training the lgbm model, I ...
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Determine most important features in diagnostic data

I have a dataset of device diagnostics. I have two tables: one relating each device to failures code. Two devices can share a failure code for example a common chip malfunction. The second table links ...
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Best Method for Using Experts to Score Qualitative Data

TL;DR: Looking for recommendations or resources on the best method to have subject matter experts score qualitative data to train ML model. Problem: I am working on a problem in the biochemistry ...
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How to interpret my logistic regression result?

I'm having a hard time to interpret my result of the logistic regression. I have a few question. Firstly, how can I check if a feature is more important to the others, like that there is a real ...
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How to retrieve results summary from statsmodels GLM with regularization?

I'm trying to fit a GLM to predict continuous variables between 0 and 1 with statsmodels. Because I have more features than data, I need to regularize. ...
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Connection between prob output LogisticReg/SVM and ROC

I have the following ROC generated using LPOCV and Logistic regression or SVM (l2 norm). Now, let's say I have a test set containing 10 patients and I get that the probabilities of those patients to ...
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Some of the p-values are NaN - logistic regression

I am trying to do logisitc regression, but have this issue - some of the p values are NaN ...
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122 views

Probability of the recipient to open the email

I am trying to build a model using logistic regression, where my dependent variable is y=1 if the mail was opened, y=0 if it was not. I have data approximately 10 records (10 rows) for every ...
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2 votes
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Interpretable models apart from Logistic Regression

I am wondering about other interpretable models apart from logistic regression. I am looking for models that can interpret the effect on the target variable by unit change in any feature variable. I ...
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Logistic Regression Model for categorical features with multiple values in each category

I am working on an insurance use case to build a logistic regression classifier to predict if a policy will lapse or not. The dataset has more than 20 categorical features for a policy. Each ...
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Different results for LogisticRegression on python 2.7 and 3

I have different results for the same kernel on python 2.7 (local machine) and python3 (the system running on kaggle) for LogisticRegression. How it is possible? Here my results from my local machine:...
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Logistic Regression with Tensorflow

Is this the correct Estimator for Logistic Regression in TF 1.10? There used to be a function called: LogisticRegressor which is deprecated In README.md file it ...
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When should the bias b be updated with weights w and when should it be updated seperately?

It seems in some Machine Learning models, the bias term $b$ is updated just like other weights $w_i, i=1...n$. For example, in Logistic Regression, using SGD, $b \ \text{or} \ w_0$ is updated with: $$...
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2 votes
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What Is The Difference Between Additive Natural Cubic Splines and Tensor Product Natural Cubic Splines?

Good day. Taking into account the picture shown, using tensor product is computationally expensive considering the fact that it has higher dimensions. I am just thinking why it is compared both ...
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Is it possible to run logistic regression on data with a few longitudinal parameters?

Currently, I am building a credit rating model based on logistic regression and faced a problem with inserting panel variables in it. Is it possible to do so in logistic regression and if it is, what ...
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101 views

What are some machine learning problems that can be attacked with continuous multiobjective optimization?

I am working on continuous vector optimization, and hence continuous multiobjective optimization is a particular case. I am interested in finding applications in machine learning for this problems. Is ...
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342 views

Learning ranking

This is a sort of a follow-up to this newbie question:Suppose I want learn ranking (so, I have a bunch of data points, ranked $1, 2, 3, ...$ Now, one way is to use something like logistic regression ...
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2 votes
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103 views

Type of Test to Determine Correlation in R

I have a dataset of approximately 48,000 rows each one a click of a an article, some of these clicks were also comments. For each article I have the country and subject of the article and name of ...
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572 views

Sales Dataset to determine best model for predicting future sales

We have a set of products in which we are trying to determine which products we should continue to sell, and which products to remove from our inventory. The file contains BOTH historical sales data ...
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Outputting risk groups for a logistic regression model

I have a problem with outputting the terms for a logistic regression model in R. For a given list of independent values, say list l of terms {w,y,z} to determine dependent variable {x}, I want to ...
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2 votes
0 answers
125 views

Random Forest Class Weighting for Logistic Probabilities

I have a model at work that I am building and am running into some odd outputs from the random forest as it pertains to the probability of response. In my case, the class distributions are very ...
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2 votes
0 answers
560 views

SAS Nested Likelihood Ratio Test for a Logistic Model

Using SAS Studio (online, student version)... Need to do a "nested likelihood ratio test" for a logistic regression. Entirety of instructions are: "Perform a nested likelihood ratio test comparing ...
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482 views

How to Interpret Multinomial Specification in R's `mnlogit` package

The mnlogit package in R allows for the fast estimation of multinomial logit models. The specification of forumlas is a bit different from most other regression ...
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Cross-sell models and additional holders

I would like to pose a question about how to treat additional holders in the propensity-to-buy models of banking products. Up to now I was only taking into considerations the clients as first holders....
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Deriving a binary logistic classifier from a multi class logistic classifier

Given a multi class logisitic classifier $f(x)=argmax(softmax(Ax + \beta))$, and a specific class of interest $y$, is it possible to construct a binary logistic classifier $g(x)=(\sigma(\alpha^T x + b)...
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Is it possible to "fine-tune" a pre-trained logistic regression model?

Fine tuning is a concept commonly used in deep learning. We may have a pre-trained model and then fine-tune it to our specific task. Does that apply to simple models, such as logistic regression? For ...
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1 vote
1 answer
37 views

Why is Word2vec regarded as a neural embedding?

In the skip-gram model, the probability that a word $w$ is part of the set of context words $\{w_o^{(i)}\}$ $(i= 1:m)$ where $m$ is the context window around the central word, is given by: $$p(w_o | ...
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1 vote
1 answer
57 views

Log odds vs Log probability

Log-odds has a linear relationship with the independent variables, which is why log-odds equals a linear equation. What about log of probability? How is it related to the independent variables? Is ...
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1 vote
1 answer
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Logistic Regression for prediction

I would like to ask about the theoretical approach of using Logistic Regression for customer data and more specifically Churn Prediction (in BigQuery and Python). I have my customer data for an online ...
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1 vote
2 answers
59 views

Odds vs Likelihood

Odds is the chance of an event occurring against the event not occurring. Likelihood is the probability of a set of parameters being supported by the data in hand. In logistic regression, we use log ...
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0 answers
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Additive model of Logit

Log converts values from multiplicative scale to additive scale. What is the advantage of an additive model in logistic regression over a multiplicative model for which we use log?
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1 vote
1 answer
24 views

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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1 vote
0 answers
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Logistic Regression-Log odds calculation example

Can someone provide me an example/link of how log odds is calculated in logistic regression (with multiple independent variables)? All the examples I've come across explain log odds calculation with a ...
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1 vote
0 answers
31 views

Meaning of 'Closed Form'

Here's an excerpt from a paper explaining Logistic Regression. What does 'Closed Form' mean in this context? Please explain in simple terms. The definitions online are confusing. Gradient of Log ...
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  • 239
1 vote
1 answer
176 views

Difficulties in create a confusion matrix in R for Yes or No

I am new to regression and confusion matrix and trying to create a confusion matrix from logistic binary regression model. I am trying to create a confusion matrix from Yes or No values from the ...
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1 vote
2 answers
177 views

MLE & Gradient Descent in Logistic Regression

In Logistic Regression, MLE is used to develop a mathematical function to estimate the model parameters, optimization techniques like Gradient Descent are used to solve this function. Can somebody ...
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2 answers
405 views

Relation between MLE (Maximum Likelihood Estimation) & Gradient Descent

What are the similarities & dissimilarities between MLE (used to find the best parameters in logistic regression) & Gradient Descent?
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  • 239
1 vote
1 answer
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How to customize logistic regression for this case?

I have a binary classification problem, with a dataset comprising of several features. When I train LogisticRegression on it, I get large number of false positives ...
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1 vote
0 answers
16 views

Complete separation of logistic regression

I came over problem that might occur in logistic regression. In case dataset is too small to observe events with low probabilities. For example, in the following table we have response set to 0 if $X\...
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1 vote
1 answer
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Finding logistic loss/negative log likelihood - binary logistic regression classification

I am new to ML and data science and am struggling with a simple problem. In my problem, I am given a series of datapoints $X_i$ where $X_i = (x_{i1}, x_{i2})$ with each data point having a label $y_i$ ...
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Linear combination of features reverses importance of all features

I am trying with a logistic model with 2 features independently or with linear combination, but in the linear combination, combining these features would reverse importance through significance levels ...
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1 vote
0 answers
24 views

Why is PyTorch's Dataloader is not inerrable?

I am working on MNIST dataset for an assignment and it seems to be I am stuck at some point for long. I have wrote my code for LogisticRegression and when I try to train the model it is not working as ...
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