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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Data Imbalance in Contextual Bandit with Thompson Sampling

I'm working with the Online Logistic Regression Algorithm (Algorithm 3) of Chapelle and Li in their paper, "An Empirical Evaluation of Thompson Sampling" (https://papers.nips.cc/paper/2011/...
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Goodness on test or train set?

I split my data set before on train (80%) and test (20%) splits. Trained logistic regression model on the train set. Now, want to check the goodness of fit using the Chi-square likelihood omnibus test,...
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Is a simple linear regression appropriate for an originally ordinal outcome variable?

Context: To form an index, I summed (and weighted) 2 variables containing ratings (1-9). Potentially problem: Wondering if it is appropriate to conduct a linear regression, all other assumptions being ...
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Predict data using Pre-Trained Classification Model

I have pre trained classification model (saved as pickle file) to predict employee attrition. My question is when I use new dataset to predict using Pickle file do I need do all preprocessing steps (...
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Using class weights with training on imbalanced dataset gives worse result w.r.t logloss than without weights

I am trying to make a model for usual binary classification that is able to predict probabilities of classes. I have not very big dataset of 10k objects where classes are imbalanced as 80:20 and tried ...
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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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LSTM basic doubt

How LSTM are able to figure out that a particular word has occurred. Like in classical algos, We have column order. But in LSTM, Since each cell receives different words, How does it know a particular ...
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NLP logistic regression

A basic doubt I have, Usually when dealing with text data for classic ML algos, We use Tf-idf which uses entire vocabulary for each row and assigns some weighted value. My doubt is can I use 5 feature ...
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How do I modify a Logistic Regression to target a specific point on the ROC curve?

From a conceptual standpoint I understand the trade off involved with the ROC curve. You can increase the accuracy of true positive predictions but you will be taking on more false positives and vise ...
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How to generate a rule-based system based on binary data?

I have a dataset where each row is a sample and each column is a binary variable. The meaning of $X_{i, j} = 1$ is that we've seen feature $j$ for sample $i$. $X_{i, j} = 0$ means that we haven't seen ...
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What to do when one feature has very large importance/weight?

I am new to Data Science and currently am trying to predict customers churn for a company that offers of subscription-based bookings management software. Its customers are gyms. I have a small ...
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The accuracy depends on the hyper-parameter in a strongly non-monotinic way

I have a data set labelled with a binary classes. I calculated the principal components from the data, then made the PC transformation. The goal is to find an optimal number of PCs so that the binary ...
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Determining increments for aggregated time series data to determine impact of individual features

I'm working with a data source that provides itemised transactions, which I am aggregating into 1 hour blocks to determine a 'rate per hour' as the dependent or target variable - i.e. like a time ...
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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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Can I perform a Logistic regression on this data?

I have the data below: I want to explain the relationship between 'Milieu' who has two factors, and 'DAM'. As you may notice, the blue population's included in the red population. Can I apply a ...
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Different results in same logistic regression model from sklearn and same dataset

I got this strange behavior when deploying my logistic regression trained in scikit-learn into production. I trained the model on my own machine and stored it in form of ...
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Exact Shap calculations for logistic regression?

Given the relatively simple form of the model of standard logistic regression. I was wondering if there is an exact calculation of shap values for logistic regressions. To be clear I am looking for a ...
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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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Verifying my understanding of MLE & Gradient Descent in Logistic Regression

Here is my understanding of the relation between MLE & Gradient Descent in Logistic Regression. Please correct me if I'm wrong: 1) MLE estimates optimal parameters by taking the partial derivative ...
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Why should MLE be considered in Logistic Regression when it cannot give a definite solution?

If MLE (Maximum Likelihood Estimation) cannot give a proper closed-form solution for the parameters in Logistic Regression, why is this method discussed so much? Why not just stick to Gradient Descent ...
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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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Possibility of close-form solution in logistic regression

In logistic regression, is a closed-form solution using MLE possible when we have data with a single independent variable? Because then, modeling the data & deriving a close-form solution wouldn't ...
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Individual P-values in Logistic Regression

I ran a logistic regression with like 10 variables (with R) and some of them have high P-values (>0.05). Should we follow the elimination techniques that we follow in multiple linear regression to ...
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Model recalibration on different dataset

I have a large dataset approximately 150k rows and 1500 of positive labels on which I can train my model for binary classification. And also I have the other dataset which is smaller and is comprised ...
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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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How to deal with missing values that are supposed to be missing?

I am trying to predict loan defaults with a fairly moderate-sized dataset. I will probably be using logistic regression and random forest. I have around 35 variables and one of them classifies the ...
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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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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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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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How do I get the mean values that are greater than .5 for my model?

I am trying to build a classification model. One of the variables called specialty has 200 values. Based on a previous post I saw, I decided I wanted to include the values that have the highest mean. ...
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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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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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Log odds understanding

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: $[0,1]$...
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Interpretation of log odds

Equation of log odds: Example: Log odds of getting a heart disease--> 0.8=2.5(Hypertension)+0.3(Gender)+0.06(Age)+15 How is this equation interpreted?
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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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Logistic Regression-Linearity between dependent & independent variables

Log of odds of the response variable being 1 has a linear relationship with the predictor variables. Hence, the log of odds is equal to the equation of a linear line. Is there any way to check the ...
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Logistic Regression - Log odds query

In logistic regression, is log odds calculated separately for each independent variable? Or is the log odds of all the independent variables combined in some way? Because, all the log odds examples I'...
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Log odds preference query

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 ...
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How Logistic Regression nomogram is constructed from binary classifier?

I' ve been reading some scientific works and I don't understand how nomograms are constructed from logistic regression models. In the article: Development and Validation of an Early Scoring System ...
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Interpretation of Log Odds in Logistic Regression

$\log(\text{odds}) = \text{logit}(P)=ln \big({{P}\over{1-P}}\big)$ $ln\big({{P}\over{1-P}}\big)=\beta_0+\beta_1x$ Consider this example: $0.7=\beta_o+\beta_1(x)+\beta_2(y)+\beta_3(z)$ How can this ...
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Industry analysis - multiple industries

I am trying to run logistic regression on marketing leads and use industry as a predictor of whether the lead converts (1/0). Often, when I enrich data from websites like crunchbase the associated ...
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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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Is there a random forest env (sci-kit, TFDF, R, etc) that has an implementation for multi-output regression?

It is easy to adapt the idea of tree based linear regression to perform logistic regression: The decision boundaries of the tree divide the space of independent variables into hyper-cubes, and each ...
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What to do about a predictor with a feature importance so high above the others that it is the only determining factor in my machine learning model?

I created a logistic regression model with scikit-learn which predicts the outcome of an NFL football game. It predicts the result based on features such as the team's record, opponent's record, pass ...
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NAN in keras neural network results

I am creating a neural network simple architecture. But I keep getting NAN in result, cant figure out why, below is my code. ...
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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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Why coefficients from logistic regression are not proportional to bad rate?

I am building a logistic regression model in Python with statsmodels.api.Logit. The model contains 12 features that are encoded using ...
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Trying to perform elastic-net regression in R

I am new to R and Elastic-Net Regression Model. I am running Elastic-Net Regression Model on the default dataset, titanic. I am trying to obtain the Alpha and Lambda values after running the train ...
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Binary Logistic Regression in R on the dataset, Titanic

I am new to R and Model Learning Algorithm. I am trying to perform Binary Logistic Regression on the training set using the Titanic dataset which is provided by default from R. I am running the ...
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Variables selection to build logistic regression model in R

I want to build a logistic regression model on this df to predict the var12 and I am trying to figure out which variables should I use and with which method.I read that I used calculate the p-value ...
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