Linked Questions

3 votes
1 answer
523 views

Effect of class imbalance on logistic regression (mathematical basis) [duplicate]

A number of posts, and papers, state that logistic regression (LR) is robust in the face of class imbalance. Unless the imbalance is extreme (e.g., events=0.01 or less), with adequate sample sizes ...
Gordon's user avatar
  • 31
0 votes
1 answer
122 views

Follow up question to: Why does logistic regression generate well-calibrated models? [duplicate]

I've read all answers and comments here: Why does logistic regression produce well-calibrated models? but still not clear about the answer. Can someone please elaborate why the following equation ...
user253288's user avatar
40 votes
5 answers
61k views

How is the cost function from Logistic Regression differentiated

I am doing the Machine Learning Stanford course on Coursera. In the chapter on Logistic Regression, the cost function is this: Then, it is differentiated here: I tried getting the derivative of the ...
bsky's user avatar
  • 1,199
10 votes
5 answers
7k views

What is calibration?

What does it mean to calibrate survey weights? Also, what are other definitions of calibration in statistics? I have heard it used in several contexts, particularly risk prediction (referring to ...
AdamO's user avatar
  • 63.8k
8 votes
1 answer
2k views

Does Regularized Logistic Regression Produce Calibrated Results?

It has been asked and addressed here that logistic regression modelling is calibrated already and there is no need for calibration of it. To me it seems the argument provided there does not follow ...
Cupitor's user avatar
  • 1,615
4 votes
0 answers
3k views

Machine learning algorithms that provide probability estimate

Question I read the post that describes the difference between classification vs. prediction. The main takeaway is that sometimes we prefer algorithms that output probabilistic estimate rather than ...
Mr.Robot's user avatar
  • 247
4 votes
1 answer
780 views

Observed probabilities in logistic regression?

Calibration is important performance metric in predictive modeling. My question is about calibration plot in logistic regression. Observed values, say, in linear model are those which are actually ...
arkiaamu's user avatar
  • 775
2 votes
1 answer
523 views

Logistic regression produces well calibrated models. Is that true for neural nets trained in batches?

This is an earlier discussion about LR producing well calibrated models: Some people equate neural net based prediction models (even deep NN or deep+sparse NN) to be equivalent to logistic ...
viggen's user avatar
  • 41
2 votes
0 answers
369 views

Calibration-in-the-small and logistic regression

It is well agreed (for example this discussion ) that Logistic Regression guarantees that model will produce well calibrated in the large (mean) predictions. Does Logistic Regression guarantees ...
viggen's user avatar
  • 41
1 vote
0 answers
121 views

Walk-forward calibration

I am training a model for time series in a walk-forward fashion: train a binary classifier on data from $t \in \{0,T-1\}$ and predict for $t=T$ (following day), then train on $t \in \{1,T\}$ and ...
shamalaia's user avatar
  • 285
0 votes
0 answers
29 views

How do we know that Logistic regression actually returns the probability [duplicate]

My question is pretty straightforward... Logistic regression returns a non linear transformation of an affine transformation of the input data, that appears to be in $(0,1)$... my question is then, ...
Alberto's user avatar
  • 1,217