Last call to make your voice heard! Our 2022 Developer Survey closes in less than a week. Take survey.

Questions tagged [regression]

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

Filter by
Sorted by
Tagged with
0 votes
0 answers
22 views

Predicting using time-series data and static data?

I have recently been working on predicting the final value of articles on Steemit.com using downloaded data. I have a large variety of features which divide into two types. Features which change over ...
user avatar
0 votes
0 answers
15 views

Which existing model could be used for wind speed and direction prediction?

I am trying to predict the wind speed and wind direction in a graph network for a geographical area. The dataset includes the start and end nodes, the distance between them, and wind speed and ...
user avatar
  • 1
1 vote
1 answer
21 views

Prediction of continuous variable based on threshold

The independent variables are date, count, atmp, and ...
user avatar
  • 111
0 votes
0 answers
11 views

Confidence intervals for regression

I have submitted a paper to arxiv and their moderator want me to write confidence interval in paper. The response i got is that ...
user avatar
0 votes
0 answers
14 views

Given a dataset with columns $x_1, x_2, \dots, x_n, y$, which algorithm can tell me which $x$ values had the biggest impact on $y$?

I have a set of data that is generated from a monolithic Monte Carlo-based program. I've set up a way to monitor the calculations of this program and output values correlating to those calculations, ...
user avatar
0 votes
0 answers
12 views

Transformers for regression on permutation of fixed size sequence?

Transformers have shown remarkable performance operating on sequences, but are equivariant to the order in the input sequence. Positional Encoding alleviates that problem, but how good is it? In my ...
user avatar
  • 246
0 votes
0 answers
21 views

Neural network have difficulty on capturing weak characteristics

I want use neural network to approximate a non-linear function. The function is, $$ F(X1,X2,X3) = A \times X1^{K1} \times exp((X1-X2) \times K2) \times exp(X3 \times K3) $$ where X1/X2/X3 are input ...
user avatar
0 votes
0 answers
14 views

Travel time between locations, two features with locations or one feature with segment

I'm working on a project where we are using supervised machine learning to predict the travel time between locations. The output value is the travel time. I'm debating whether to have two features or ...
user avatar
0 votes
0 answers
16 views

What is the canocial way to handling differing input and output dimensions for the transformer model?

I have an essential regression task, where the input is of dimension $d$ and the output is a scalar. I think the transformer model is a good fit for this problem. In the vanilla multi-head-attention ...
user avatar
  • 41
0 votes
0 answers
16 views

What can be the reasons for validation MSE < training MSE at beginning of training and network failing to generalize afterwards?

I am using a Convolutional Neural Network for regressing time series data. The objective is to predict an obfuscated metric. The training metrics and losses are as follows. The val_loss is lower than ...
user avatar
0 votes
1 answer
70 views

How to make a proper approximation of Sine function with Neural Networks?

TL;DR; How to build a neural network that properly approximates the sine function with different ranges? Context and Question: From this question I decided to use the Sergey's answer, however I used a ...
user avatar
  • 111
3 votes
1 answer
59 views

Why does my regression-NN completely fail to predict some points?

I would like to train a NN in order to approximate an unknown function $y = f(x_1,x_2)$. I have a lot of measurements $y = [y_1,\dots,y_K]$ (with K that could be in the range of 10-100 thousands) ...
user avatar
  • 31
0 votes
1 answer
83 views

What's the best model to use for CNN(deep learning) regression task for small image dataset?

What are the best Deep learning models(with how many layers) to use in a regression task for a custom dataset containing around 100 images of only one object per image which is more or less ...
user avatar
1 vote
1 answer
29 views

Does a second-order fully-connected layer have any uses?

I was thinking about implementing second-order regression via a fully-connected layer, and I came up with this: $X$ is the input data, shaped $(features, batch\_number)$. $w0$ is the bias, shaped $(...
user avatar
  • 113
-1 votes
1 answer
54 views

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 ...
user avatar
0 votes
0 answers
10 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 ...
user avatar
1 vote
0 answers
53 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 ...
user avatar
0 votes
1 answer
66 views

Do I need to tune the hyper-parameters or more data if SVR model performs poorly?

I am using non-linear data to SVR and have tried tuning the hyperparameters and still have a poor model performance. Do I need more data or format the data for more suitable results? I get similar ...
user avatar
1 vote
0 answers
13 views

What is the best way to train a text-based regressor model?

I want to build a deep learning model that can predict a continuous value (LogP in this case) given text inputs (SMILES notations in this case), the dataset is as illustrated below. SMILES notations ...
user avatar
1 vote
1 answer
31 views

Best way to measure regression accuracy?

I'm asking because classification problems have very concrete metrics like accuracy that are totally transparent to understand. Whereas regression models seem to have a very large number of possible ...
user avatar
  • 266
0 votes
0 answers
7 views

Training and sampling for static model in multivariate time series

Let's suppose I have two time series $x_t$ and $y_t$. I also assume there is an underlying static model of the form: $$ y_t=f(x_t) + \epsilon_t $$ As I said I consider the model a static model meaning ...
user avatar
0 votes
1 answer
56 views

How to pass multiple vectors and numeric features as input to the neural network?

I need help in a regression scenario. I have 12 input features. 4 of which are coordinates (each is a vector) in XYZ plane ...
user avatar
0 votes
0 answers
12 views

Are ranking models considered discriminative?

I'm developing a model that ranks entries based on cosine similarity to a query. Since it doesn't actually define a boundary between x and y I initially believed that such ranking models are not ...
user avatar
  • 1
0 votes
0 answers
15 views

Feature selection by Simple regression vs finite impulse response (FIR) method (on TIME Series analysis)

We are working on prediction one company production estimation and the main field of works is like stock market prediction(Time series analysis and process data). So I have some comment on using ...
user avatar
0 votes
0 answers
26 views

Regress values inside the bounding boxes to predict a value in Object Detection

I am currently working on an object detection task. I have a dataset of Grayscale and Depth Images. The annotation format is x1, y1, x2, y2, class, depth. I have calculated this depth (of each object/...
user avatar
  • 1
0 votes
1 answer
63 views

Which loss function could I use to solve a regression problem as a classification problem (where we discretize the labels into buckets)?

I am considering a rather typical regression problem, but, for practice, I am trying to implement this as a classification problem. The setup is as follows. I have $\mathbb{R}$-valued labels $y_i \in [...
user avatar
0 votes
0 answers
26 views

Regression for a discrete variable

I'm building a model (neural net) that would predict a quality score for images. Ground truth is given by a 4-level discrete variable (0%, 33%, 67%, 100%), and I would like to build a model that would ...
user avatar
0 votes
1 answer
45 views

How to explain that a same DNN model have radically different behaviours with each new initialization and training?

I'm trying to predict the continuous values of a variable $y$ using a Fully Connected Neural Network while providing it with data from a $(3300, 13)$ matrix $X$ where $X[i, :]=[0,...,1,...,0,x_{i}]$. ...
user avatar
  • 489
0 votes
0 answers
26 views

Is it possible to use RGB image with decimal values when feeding training data to CNN?

I am working with four grayscale images of float32 data type to perform regression using Keras. Three images are stacked using np.dstack to form a RGB data-set. The ...
user avatar
  • 1
3 votes
2 answers
118 views

Can predictions of a neural network using ReLU activation be non-linear (i.e. follow the pattern) outside of the scope of trained data?

Training on a quadratic function x = np.linspace(-10, 10, num=1000) np.random.shuffle(x) y = x**2 Will predict an expected quadratic curve between ...
user avatar
2 votes
1 answer
168 views

How to get more accuracy of the logistic regression model?

I am working on a Baby Crying Detection model using logistic regression. Out of $581$ audios, $222$ are of a baby crying. Each audio is of $5$ seconds. what I have done is convert each audio into ...
user avatar
0 votes
0 answers
58 views

Predicting the probability of a periodically happening event occurring at a given time

I have encountered this problem on how to predict the probability of a periodically happening event occurring at a given time. For example, we have an event called being_an_undergrad. There are many ...
user avatar
0 votes
1 answer
148 views

How to forecast multiple target attributes in Python?

I need to forecast two non-correlated time-series (non-stationary). A sample is presented below: ...
user avatar
0 votes
0 answers
41 views

Multi-target regression using scikit-learn without ytrain

I would like to use the multi-target regression with scikit-learn. However, the examples I've seen use Xtrain and ytrain? What is ytrain in regression? I know y it is used for classes in ...
user avatar
2 votes
1 answer
1k views

Why is no activation function needed for the output layer of a neural network for regression?

I'm a bit confused about the activation function in the output layer of a neural network trained for regression. In most tutorials, the output layer uses "sigmoid" to bring the results back ...
user avatar
  • 167
4 votes
1 answer
116 views

How to define machine learning to cover clustering, classification, and regression?

How to define machine learning to cover clustering, classification, and regression? What unites these problems?
user avatar
  • 171
1 vote
1 answer
61 views

Handling imbalanced data with multiple targets

I have the model which has 3 outputs (it is a regression task, I have the angle of the steering wheel, brake and acceleration). I can divide my values to some smaller bins and in this way I can change ...
user avatar
  • 173
1 vote
0 answers
49 views

How to restrain a model's outputs to a certain range without affecting its representative capacity?

CONTEXT I am trying to build a regression model that finds the optimal parameters for a given input. The data I am using are point clouds, with N points and ...
user avatar
  • 111
-1 votes
1 answer
64 views

What model to use to get a robust model to predict next 3 days of sales even for products that have just sold once ever?

PROJECT: I am working on an e-commerce site where digital products can run out so there is need to reorder them 72h before they run out (reordering them sooner is not a problem but having notification ...
user avatar
  • 101
2 votes
1 answer
55 views

Which NN would you choose to estimate a continuous function $f:\mathbb R^2 \rightarrow \mathbb R$?

Suppose we want to estimate a continuous function $f:\mathbb R^2 \rightarrow \mathbb R$ based on a sample using a NN (around 1000 examples). This function is not bounded. Which architecture would you ...
user avatar
0 votes
0 answers
48 views

How should I use deep learning to find the rotation of an object from its 2D image?

I have 6600 images and I am supposed to know the rotation of the object in each image. So, given an image, I want to regress to a single value. My attempt: I use Resnet-18 to extract a feature vector ...
user avatar
1 vote
0 answers
27 views

Neural network architecture with inputs and outputs being an unkown function each

I am trying to set up a neural network architecture that is able to learn the points of one function (blue curves) from the points of an other one (red curves). I think that it could be somehow ...
user avatar
1 vote
0 answers
38 views

How to find a parameter combination for a black box using AI?

I am working on a project where I encountered a component which takes 96 arguments (all integer values) and outputs 12 float values. I would like to find a useful combination of these 96 values to ...
user avatar
  • 186
1 vote
1 answer
119 views

When are multiple hidden layers necessary?

I know that my question probably seems like being asked many times, but Ill try to be more speciffic: Limitations to my question: I am NOT asking about convolutional neural networks, so please, try ...
user avatar
  • 131
1 vote
1 answer
67 views

What is the best algorithm to solve the regression problem of predicting the number of languages a Wikipedia article can be translated to?

I'm doing a student project where I construct a model predicting the number of languages that a given Wikipedia article is translated into (for example, the article TOYOTA is translated into 93 ...
user avatar
  • 11
0 votes
1 answer
129 views

How to use validation dataset in my logistic regression model?

I am new to machine learning and recently I joined a course where I was given a logistic regression assignment in which I had to split 20% of the training dataset for the validation dataset and then ...
user avatar
1 vote
0 answers
25 views

How do you make a regression model from a binary labeled dataset?

Suppose I have a dataset with hand images. Hand completely opened is labeled as 0 and hand completely closed (fist) are labeled as 1. I also have a bunch of unlabeled images of hands which, if ...
user avatar
  • 315
0 votes
2 answers
101 views

What ML algorithm should I use that suits this data?

What if I have some data, let's say I'm trying to answer if education level and IQ affect earnings, and I want to analyze this data and put in a regression model to predict earnings based on the IQ ...
user avatar
0 votes
0 answers
63 views

Finding the 'ultimate resolution' of an ANN

I want to use a neural network to predict the refractive index of a solution. My thinking is, instead of immediately training on many samples, I will first find the 'ultimate resolution' of the ...
user avatar
  • 133
2 votes
0 answers
35 views

Is there a classification task with multiple attribute regression?

I'm trying to look for a task that predicts a discrete label first (classification), and then predicts the multiple continuous attributes of the predicted class. I found some papers about multi-output ...
user avatar