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### Selecting Binary Classification Probability Threshold [duplicate]

I have a binary classification problem I have modeled and I'm trying to determine the best way to select my probability threshold. Here was my modeling approach: Create a training and testing set. My ...
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### Why is logistic regression called a machine learning algorithm?

If I understood correctly, in a machine learning algorithm, the model has to learn from its experience, i.e when the model gives the wrong prediction for the new cases, it must adapt to the new ...
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### When should I balance classes in a training data set?

I had an online course, where I learned, that unbalanced classes in the training data might lead to problems, because classification algorithms go for the majority rule, as it gives good results if ...
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### Logistic regression: maximizing true positives - false positives

I have a logistic regression model (fit via glmnet in R with elastic net regularization), and I would like to maximize the difference between true positives and false positives. In order to do this, ...
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### Philosophical question on logistic regression: why isn't the optimal threshold value trained?

Usually in logistic regression, we fit a model and get some predictions on the training set. We then cross-validate on those training predictions (something like here) and decide the optimal threshold ...
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Hinge loss can be defined using $\text{max}(0, 1-y_i\mathbf{w}^T\mathbf{x}_i)$ and the log loss can be defined as $\text{log}(1 + \exp(-y_i\mathbf{w}^T\mathbf{x}_i))$ I have the following questions: ...
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### When is logistic regression suitable?

I'm currently teaching myself how to do classification, and specifically I'm looking at three methods: support vector machines, neural networks, and logistic regression. What I am trying to understand ...
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### regression for binary classification

Given a binary classification problem, is there any inherent difference (or advantage) to using a classifier (say a logistic regression) and a regression, where the classes are denoted by 0 and 1 (or ...
18k views

### Adjusting probability threshold for sklearn's logistic regression model

I am a 10th grade student working on a binary classification problem and I have decided to use the logistic regression model from Scikit-Learn. I am looking to predict patient adherence given the time ...
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### Assessing logistic regression models

This question arises from my actual confusion about how to decide if a logistic model is good enough. I have models that use the state of pairs individual-project two years after they are formed as a ...
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### How do you predict a response category given an ordinal logistic regression model?

I want to predict a health problem. I have 3 outcome categories that are ordered: 'normal', 'mild', and 'severe'. I wish to predict this from two predictor variables, a test result (a continuous, ...
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### Logistic regression: maximum likelihood vs misclassification

Traditionally the fitting of the logistic regression function is explained using maximum likelihood. Could one fit the logistic regression function as well based on either least-squares or based on ...
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### Loss vs. Classification Accuracy in applied problems

In practical problems, where we want to for instance predict if a subject has a certain disease or not, we usually take classification accuracy as a measure which has the straightforward ...