18k views

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 ...
29k views

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 ...
14k views

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, ...
2k views

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 ...
14k views

hinge loss vs logistic loss advantages and disadvantages/limitations

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: ...
6k views

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 ...
3k views

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 ...
7k views

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 ...
8k views

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, ...
4k views

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 ...
2k views

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 ...
8k 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 ...
4k views

Why is Logistic Regression mentioned by many sources as useful in predicting stock prices?

My understanding of Logistic Regression is that it is actually a classifier, hence used for predicting either a categorical outcome (ie. binary or an outcome with specific labels) as opposed to a ...