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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9
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3answers
21k 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 ...
19
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3answers
8k 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}...
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2answers
1k 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 ...
7
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1answer
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 ...
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2answers
111 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 ...
13
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1answer
6k 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 ...
5
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2answers
154 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 ...
3
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3answers
393 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 ...
26
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3answers
100k 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 ...
12
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2answers
3k 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, ...
6
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2answers
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, ...
5
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2answers
287 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 ...
10
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4answers
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 ...
8
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3answers
7k 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 (...
7
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2answers
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 ...
3
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4answers
194 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 ...
2
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1answer
1k 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 ...
2
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0answers
365 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 ...
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0answers
48 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): ...
2
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1answer
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' ...
2
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2answers
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/...
2
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1answer
260 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 ...