Linked Questions
15 questions linked to/from Regression as classification: advantages?
103
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9
answers
35k
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If mean is so sensitive, why use it in the first place?
It is a known fact that median is resistant to outliers. If that is the case, when and why would we use the mean in the first place?
One thing I can think of perhaps is to understand the presence of ...
- 4,552
15
votes
5
answers
10k
views
Why should binning be avoided at all costs?
So I've read a few posts about why binning should always be avoided. A popular reference for that claim being this link (updated link).
The main getaway being that the binning points (or cutpoints) ...
- 151
7
votes
3
answers
2k
views
How much of neural network overconfidence in predictions can be attributed to modelers optimizing threshold-based metrics?
Neural network "classifiers" output probability scores, and when they are optimized via crossentropy loss (common) or another proper scoring rule, they are optimized in expectation by the ...
- 67.1k
4
votes
2
answers
438
views
Is the proportion classified correctly a reasonable analogue of $R^2$ for a classification model?
Let's do some classification and evaluate the prediction quality. The easiest metric to understand is the prediction accuracy, which can be reported as the proportion classified correctly to put the ...
- 67.1k
10
votes
2
answers
789
views
How to train a model when instead of a target we have a range where it is?
Often in machine learning we have a situation when target is numeric (real or integer). Each target comes with an associated input vector. The goal is to learn the mapping from the input vectors to ...
- 724
1
vote
1
answer
488
views
Measuring the Performance of Logistic Regression: Regression vs Classification
I have noticed that Logistic Regression (https://en.wikipedia.org/wiki/Logistic_regression) is a model that used significantly for both Regression problems and Classification problems.
When used for ...
2
votes
2
answers
544
views
Does Multinomial Probability Calibration Consider the Probabilities of the Non-Dominant Classes?
The gist behind Harrell's rms::calibrate function makes sense to me. While I have yet to understand the magic that lets us calculate the "true" ...
- 67.1k
2
votes
2
answers
410
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using (deep) neural networks for a severely imbalanced image dataset when some classes have <10 images
Taking a long shot here. So I have a a small dataset of ~500 images with discrete labels from 1 to 9. My task is to detect the per-class and overall accuracy of this classification method using a (...
- 524
2
votes
1
answer
417
views
How do I change my neural network from a classification task to a regression task?
I currently have a neural network that is doing a reasonable job in classifying an image into a number of classes although not that great. These classes however are essentially buckets on a floating ...
- 51
1
vote
1
answer
537
views
Evaluation metric for time-series anomaly detection
My dataset is time-series sensor data and anomaly ratio is between 5% and 6%
1.
For time-series anomaly detection evaluation, which one is better, precision/recall/F1 or ROC-AUC ?
When empirically ...
2
votes
2
answers
413
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ML Method for directional forecast
I've uni-variate demand data (Weekly data for 2 years), and wish to do a directional forecast based on the data. Magnitude of the forecast is not important here, but directional accuracy is of ...
- 41
3
votes
2
answers
70
views
Transform a regression problem in a classification problem and express it ouput as probability function
I am dealing with a regression problem related to predict the demand of a item by customers. This demand is an integer from 0 to 100 for example. I can employ SVR, Random Forest Regressor, etc. in ...
- 141
2
votes
1
answer
118
views
Continous vs categorical predictions
I have been thinking about this recently.
I want to predict tomorrows price for a certain stock, lets say apple. For this I can use many different models; regression analysis, random forest, RNN... ...
- 103
2
votes
0
answers
58
views
Strategies for adapting classification algorithm to regression problems
I am the author of the OneR package and more and more people approach me asking how to use this classification algorithm for regression problems.
At the moment two approaches exist:
either the ...
- 6,246
0
votes
0
answers
195
views
Regression with text data
My goal is to create a regression model with text data where encoded text predicts a value, (news headlines, or article summaries, predicting number of clicks). The y is very left-skewed (few articles ...
- 131