# Questions tagged [logistic-regression]

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

28 questions
Filter by
Sorted by
Tagged with
9k views

### Linear regression with non-symmetric cost function?

I want to predict some value $Y(x)$ and I am trying to get some prediction $\hat Y(x)$ that optimizes between being as low as possible, but still being larger than $Y(x)$. In other words: \text{cost}...
28k views

### How to plot logistic regression decision boundary?

I am running logistic regression on a small dataset which looks like this: After implementing gradient descent and the cost function, I am getting a 100% accuracy in the prediction stage, However I ...
• 291
1 vote
2k views

### Finding optimal weights for models

I'm trying to implement an algorithm to find the minimal value of a function. Before moving to sigmoid activation functions, i'm trying to understand linear regression. Usually, a gradient descent ...
• 389
8k views

### What is the difference in xgboost binary:logistic and reg:logistic

What is the difference in R in xgboost between binary:logistic and reg:logistic? Is it only in evaluation metric? If yes, how does RMSE on binary classification compare to error rate? Is the ...
• 297
4k views

### Why does logistic regression in Spark and R return different models for the same data?

I've compared the logistic regression models on R (glm) and on Spark (LogisticRegressionWithLBFGS) on a dataset of 390 obs. of ...
• 113
212 views

### Confused AUC ROC score

I am working on binary classification problem, I try to evaluate the performance of some classification algorithms (LR,Decission Tree , Random forest ...). I am using a cross validation technique (to ...
• 103
576 views

### Model performance worsens after Cross Validation

I am training a logistic regression model on a dataset with only numerical features. I performed the following steps:- 1.) heatmap to remove collinearity between variables 2.) scaling using ...
• 1,331
215 views

### Feature Importance without Random Forest Feature Importances

Is their an intuitive way of finding feature importances without just using the random forest feature importances method? I have a binary logistic regression problem where I have binary features (1 or ...
• 271
1 vote
123 views

### Regression Algorithms in Production

I am interested in predicting if a doctor would prescribe a specific drug and have chosen Logistic Regression as a starting point. I have a few questions: Is feature selection the first step to take ...
• 11
122k views

### How to get p-value and confident interval in LogisticRegression with sklearn?

I am building a multinomial logistic regression with sklearn (LogisticRegression). But after it finishes, how can I get a p-value and confident interval of my model? It only appears that sklearn only ...
• 401
4k views

### The differences between SVM and Logistic Regression

I am reading about SVM and I've faced to the point that non-kernelized SVMs are nothing more than linear separators. Therefore, ...
• 5,921
2k views

### Is this a good practice of feature engineering?

I have a practical question about feature engineering... say I want to predict house prices by using logistic regression and used a bunch of features including zip code. Then by checking the feature ...
• 797
8k views

### What cost function and penalty are suitable for imbalanced datasets?

For an imbalanced data set, is it better to choose an L1 or L2 regularization? Is there a cost function more suitable for imbalanced datasets to improve the model score (...
• 101
6k views

### Regression model to predict probability of rare event

I have a dataset with around 900.000 records, around 1000 of which are marked as positive (the studied event occurred). The probability of the event occurring is always low (i.e. < 0.1), and I ...
• 71
2k views

### Voting combined results from different classifiers gave bad accuracy

I used following classifiers along with their accuracies: Random forest - 85 % SVM - 78 % Adaboost - 82% Logistic regression - 80% When I used voting from above classifiers for final classification, ...
• 367
435 views

### How to adjust cofounders in Logistic regression?

I have a binary classification problem where I apply logistic regression. I have a set of features that are found significant. But I understand that Logistic regression doesn't consider feature ...
• 2,449
877 views

### Why are deep learning models unstable compare to machine learning models?

I would like to understand why deep learning models are so unstable. Suppose I use the same dataset to train a machine learning model multiple times (for example logistic regression) and a deep ...
• 151
1k views

### 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 ...
• 157
3k views

### Updating One-Hot Encoding to account for new categories

My question is focused around how to appropriately update an encoded feature set when a new category is introduced by the test data. I use the data in logistic regression and I know it is not a 'live' ...
• 181
450 views

### difference between feature interactions and confounding variables

Let me define the problem space. I am working a binary classification problem. I am trying to build a causal model as well as predictive model. My aim is to find list of significant features (based ...
• 2,449
2k views

### Normal distribution instead of Logistic distribution for classification

Logistic regression, based on the logistic function $\sigma(x) = \frac{1}{1 + \exp(-x)}$, can be seen as a hypothesis testing problem. Where the reference distribution is the standard Logistic ...
• 159
1k views

### What is the purpose of Logit function? At what stage of model building process this logit function is used?

We have two prominent functions (or we can say equations) in logistic regression algorithm: 1. Logistic regression function. 2. Logit function. I would like to know: a. Which of these equation(s) is/...
• 21
1 vote
86 views

### Normalizing and joining of independent logistic regression model's prediction

I need to train several Logistic regression models on a different set of data (with a different set of labels): ...
• 131
1 vote
53 views

### When it is okay to stick with low performance models?

I posted here already but it is marked to close, so thought of posting it here (as this might be the right forum) Am working on a simple logistic regression with 1000 records and 28 features. My ...
• 2,449
1 vote
1k views

### Why do we need the sigmoid function in logistic regression?

What is the purpose of the logistic sigmoid function as it is used in logistic regression? Why does it need to be part of the hypothesis function h(x) ? As I understand it, the logistic sigmoid ...
• 33
123 views

### 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 ...
• 273
I have a data set of 1 million points and 30 features. The output variable has multiple classes (1 to $n$) but the problem I'm interested in is only concerned whether the output belongs to class 1 or ...