# Questions tagged [regression]

For questions related to regression (both linear and non-linear) in the context of machine learning and AI.

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### What are the consequences when we multiply, instead of add, a penalty term?

The typical objective function in regression problems like Lasso or Ridge includes a Residual Sum of Squares (RSS) term added to a penalty term based on a norm of the coefficients. What are the ...
51 views

### Regression loss conditioned by the ground-truth values

I'm working on a regression problem with a CNN in which the input is a single image, and the output is an angle in degrees (which determines a specific measure related to the image). Sometimes, the ...
1 vote
40 views

### What number classes makes a classification problem continuous

I am working on a classification problem, where I have sequences of images and I want to train a model to predict the index of the image with some wanted property. The target classes would obviously ...
25 views

### How to make a RandomForestRegressor learn to differentiatie similar inputs with different outputs?

I'm working on a regression task with Sklearn RandomForestRegressor and I'm having some trouble distinguishing between two similar data with very different expected outputs. For example, each pair of ...
65 views

### Does the accuracy of a regression learner depend on the way we feed data?

Consider a plot of points as such: As one notices, this looks like an alternating sequence. Further, it can be divided into two subsequences as $a_{\text{odd}}$ and $a_{\text{even}}$ as they seem to ...
25 views

### Pixel-wise regression only focus on edge

I am trying to use unet to learn pixel-wise regression from one image to one groundtruth with the same image size. The network seems to focus too much on the edge of the image, and it does not learn ...
16 views

### Sparse linear discriminant analysis for regression problem?

So far, Linear Discriminant Analysis has beed used for classification problems http://proceedings.mlr.press/v38/wu15.pdf . I wonder if there are any ways to adapt it to regression problems?
1 vote
34 views

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### How to create a global forecasting model using deep learning?

I am aiming to build a global/general forecasting model (don't know what's the proper terminology) using deep learning. The idea behind this is to create a model trained on several time series that ...
33 views

### How to output a function given a time series data as an input using supervised learning?

I have a spreadsheet with time series data collected from two sensors, one measuring temperature and the other measuring humidity. And I also collected data from an experiment that I conducted, the ...
142 views

### Why is a simple regression problem so hard for an MLP to learn?

Consider a very simple problem, which is to find the maximum value out of a list of 5 numbers between 0 and 1. This is obviously trivial, but serves as a good example for a real-world problem I'm ...
34 views

### Multi-output regression problems

I am training CNNs on 3D image data (dimensions [500, 512, 512]) to locate 7 3D points inside the image. I have thought of two different ways to solve this problem, ...
58 views

### Is there any interpretation method suitable for CNNs which do regression tasks?

I mainly tackle regression problems by CNNs, and want to find a reliable method to calculate the heatmaps for NN's results. However, I find almost all interpretation methods including CAM is used for ...
19 views

### What's a good regression algorithm for handling tabular data that have categorical data, "list of words"

Problem statement: I want to predict future prices of trips based on historical pricing data. I'm looking for an algorithm that has the following features: Unsupervised algorithm Limit the amount of ...
1 vote
28 views

### bad prediction when having noise on the data: LSTM time-series regression

I want to predict the force plate using a smart insole using the LSTM model for time series prediction. the data on the force plate has positive and negative values (I think the resulting positive ...
443 views

### Why does GridSearchCV model give worse results despite same parameters used with base model

I am trying to make prediction using random forest regression and then utilize GridSearchCV to tune hyperparameters(just 'n_estimators'). However results of GridSearchCV are worse than base model. ...
48 views

### Multi-layer network only predicts linear trends

I have made a neural network from scratch (in java), which is refusing to switch out of linear regression. I have pushed up the layer sizes (it now has 2 hidden layers, both with 5 neurons), and yet ...
1 vote
336 views

### Out of distribution detection (OOD) in the context of regression problems

I'm working in a regression setting to predict a scalar value $y$ from an input $\textbf{x} \in \mathbb{R}^D$ and I'm interested in understanding whenever my model is fed with something that it is ...
1 vote
42 views

### How do I interpret this loss function?

In this AI note from https://deeplearning.ai, the loss function below is used for a regression problem. However, I don't know how to interpret this loss function. First, does the author take the ...
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### Is my dataset a time series dataset? and should I use an LSTM?

I have a dataset where I am recording temperature after every 4milliseconds till 500 and another feature "conductivity value". The length of the dataset is around a 1000 rows. I need to find ...
15 views

### What to predict in a limited transaction dataset?

I have been given a task with a real transaction dataset. The task is to predict something using either logistic regression or simple binary classification. The columns are as follow: Transaction ID ...
1 vote
66 views

### Is the VC dimension of a MLP regressor a valid upper bound on how many points it can exactly fit?

I want to calculate an upper bound on how many training points an MLP regressor can fit with ~0 error. I don't care about the test error, I want to overfit as much as possible the (few) training ...