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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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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 ...
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 ...
• 1
1 vote
21 views

Prediction of continuous variable based on threshold

The independent variables are date, count, atmp, and ...
• 111
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 ...
• 123
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, ...
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 ...
• 246
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 ...
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 ...
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 ...
• 41
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 ...
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 ...
• 111
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) ...
• 31
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 ...
1 vote
29 views

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 ...
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 $X[i, :]=[0,...,1,...,0,x_{i}]$. ...
• 489
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 ...
• 1
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 ...
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 ...
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 ...
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: ...
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 ...
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 ...
• 167
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?
• 171
1 vote
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 ...
• 173
1 vote
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 ...
• 111
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 ...
• 101
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 ...
• 121
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 ...
1 vote
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 ...
1 vote
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 ...
• 186
1 vote
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 ...
• 131
1 vote
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 ...
• 11
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 ...
1 vote
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 ...
• 315
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 ...
• 123