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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32 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 ...
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30 views

Problem fitting data using mlpregressor

I'm training a sklearn.neural_network.mlpregressor by a large data of students performance (an excel file with 740 students and 27 columns that are their qualities) and I want to predict their grades. ...
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41 views

GPU-training on Google Cloud Platform slower than CPU-training

I recently trained Kaggles "Advanced Housing Prices"-Competition using Catboost. For training i used a compute-instance on Google Cloud Platform (GCP) (CPU: Xeon Quad-Core, RAM: 15GB, GPU: ...
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58 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 ...
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16 views

How to figure out loss weight for label-imbalanced regression problems?

In classification, suppose you have 1 image labeled as cancer and 99 labeled as not cancer, you can just divide the loss weight of "not cancer" by 99. Then you can train the model as this ...
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29 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 ...
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37 views

What work has been done with Poisson-style regression via neural networks with exponential activation function?

The first neural net I wrote was a classifier. After that, I learned that neural nets can be used for regression tasks, even quantile regression. It has become clear to me that the usual games with ...
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0answers
43 views

Which activation functions should I use for polynomial regression?

I am a beginner in machine learning and neural networks. I have only used neural networks for classification problems. My aim is to modify it so that it can work for polynomial regression as well. In ...
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24 views

3d representation of a regression with two independent variables one of them is categorical and another is continuous

I have hopefully a fundamental question of Do I understand things right. (Thank you in advance and sorry for my English which might be not so good) 1-Preambula 1: I know that if we have 2 independent ...
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32 views

Can SqueezeNet be used for regression?

I want a model that outputs the pixel coordinates of the tip of my forefinger, and whether it's touching something or not. Those would be 3 output neurons: 2 for the X-Y coordinates and 1, with a ...
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41 views

How MSE should be appliead with multi target deep network?

I'm having a problem understanding how the MSE should be used when working with a multidimensional target, e.g 3 dimensiones. (My outputs are continuois values, not categorical) Let us say I have a ...
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31 views

Can a neural network whose output is uniformly equal to zero learn its way out of it?

I am performing a regression task on sparse images. The images are a result of a physical process with meaningful parameters (actually, they are a superposition of cone-like shapes), and I am trying ...
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34 views

Non-linear regression with a neural network

I have to perform a regression on three curves as shown in the following plot. Here, accA (y-axis) is the dependent variable, and ...
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48 views

Which one is better: multivariate regression with basis expansion or neural networks?

Assume we are given a training dataset $D = \{ (x_i, y_i)\}_{i=1}^{N}$. My question is: which is better? A multivariate regression with basis expansion with independent matrix $X$ and dependent ...
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27 views

Boston Housing Current Best Model

I'm currently looking for a standard data-set to test some new regression models I've been developing on, and the "Boston Housing Prices" data-set seemed to stand out, since it's very standard. ...
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3answers
132 views

Using sigmoid in LSTM network for multi-step forecasting

I'm trying to develop a multistep forecasting model using LSTM Network. The model takes three times steps as input and predicting two time_steps. both input and output columns are normalised using ...
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1answer
50 views

What is the type of problem requiring to rate images on a scale?

I'm new to the topic, but I've used some off the shelf knowledge about computer vision for classifying images. For example, you can easily generate labels that can determine whether or not e.g. a ...
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1answer
63 views

Is a basic neural network architecture better with small datasets?

I'm currently trying to predict 1 output value with 52 input values. The problem is that I only have around 100 rows of data that I can use. Will I get more accurate results when I use a small ...
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2answers
96 views

Is there a possibility that there is no relationship between some inputs and outputs?

I'm doing machine learning projects. I took a look at many datasets I worked with, mostly there are already famous datasets that everyone uses. Let's say I decided to make my own dataset. Is there a ...
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1answer
43 views

How does the regression layer in the localization network of a spatial transformer work?

I am trying to understand the spatial transformer network mentioned in this paper https://papers.nips.cc/paper/5854-spatial-transformer-networks.pdf. I am clear about the last two stages of the ...
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1answer
40 views

Have GANs been used to solve regression problems?

I've noticed that in the last 2 years GANs have become really popular. I know that initially they have been proposed for image classification but I was curious if any of you are aware of any papers ...
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43 views

Predicting population density from satellite imagery

I have very high resolution images from LANDSAT 8 (5 out of 12 bands), which are of various administrative regions of a country. Each image is of variable dimensions, but generally of the order of [...
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1answer
74 views

Which is a better form of regularization: lasso (L1) or ridge (L2)?

Given a ridge and a lasso regularizer, which one should be chosen for better performance? An intuitive graphical explanation (intersection of the elliptical contours of the loss function with the ...
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1answer
68 views

Possible model to use to find pixel locations of objects

I want to make a model that outputs the centre pixel of objects appearing in an image. My current method involves using a CNN with L2 loss to output an image of equivalent size to the input where ...
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1answer
33 views

Predicting a day's data

I have a dataset containing timestamp and temperature. For each day, I have 1440 values viz., I have data for every minute of that day(60minutes * 24hrs = 1440). The Dataset looks like this: As an ...
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1answer
60 views

Finding the optimal combination of inputs which return maximal output

I am currently working on a problem and now got stuck to implement one of it's steps. This is a simple attempt to explain what I am currently facing, which is something that I am aiming to implement ...
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4answers
93 views

Regression using neural network

I'd like to ask for any kind of assistance regarding the following problem: I was given the following training data: 100 numbers, each one is a parameter, they together define a number X(also given)....
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20 views

Optimisation of dependence of efficiency of CNN on training data

I got a large dataset of images (dimensions of 16 x 16, 250k samples) and corresponding spherical coordinates (distributed uniformly in each coordinate). On these, I trained a convolutional regression ...
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38 views

How to perform regression with multiple numeric (positive and negative) inputs and one numeric output?

I have a dataset with different types of numerical values (both negative and positive numerical values) for the inputs (for example, -40, -35, 1, 25, 39, etc., that is, multiple inputs) and single ...
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0answers
21 views

Which algorithm and architecture to use for 1:1 matrix transformation of an 8X8 dimension?

I would like to map the simplest 8X8 matrices, one to one, but am not sure which AI algorithm would give the best performance. I am thinking about the DeepLearning4j, however, I don't know which ...
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0answers
15 views

What is a good model for regression problem with binary features and small data?

I am trying to predict the solution time for riddles in which matchsticks are combined into digits and operators. An example of a matchstick riddle is 4-2=8. The solution for this riddle would be ...
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0answers
25 views

Choosing neural network output for prediction (regression) of a dynamical system

I’m trying to train a neural network to approximate the output of a dynamical system $dy/dt=f\left(y(t), u(t) \right)$, namely, given $y(0)$ and $u(t_i), i=1,2...N$ I want the network to predict $y(...
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1answer
61 views

Imposing physical constraints (previous knowledge) in a neural network for regression

I'm trying to train a neural network to do a multiple non-linear regression $y=f(x_i), i=1,2…N$. So far it works good (low MSE), but some predictions $y$ are “non-physical”, for instance for our ...
2
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1answer
66 views

Pose estimation using CNNs on Point clouds

In the case of single shot detection of point clouds, that is the point cloud of an object is taken only from one camera view without any registration. Can a Convolutional Network estimate the 6d pose ...
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1answer
75 views

When should I create a custom loss function?

I'm using a neural network to solve a multi regression problem because I'm trying to predict continuous values. To be more specific, I'm making a tracking algorithm to track the position of an object, ...
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1answer
80 views

What is the best approach for multivariable and multivariate regression?

I want to build a multivariable and multivariate regression model in Keras (with TensorFlow as backend), that is, a regression model with multiple values as input (multivariable) and output (...
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2answers
147 views

How do we choose the activation function for each hidden node? [duplicate]

I am new to neural networks. I would like to use them as a fitting or forecasting method. A simple NN model that does not contain hidden layers, that is, the input nodes are directly connected to the ...
2
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0answers
19 views

How to choose the suitable Neural Network Architecture for Regression Tasks

so I'm working on a Project where I want to predict the Vehicle Position from the Vehicle Data like speed, acceleration etc.. now the data that I have comes also with a timestamp for each sample ( I ...
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1answer
60 views

TF Keras: How to turn this probability-based classifier into single-output-neuron label-based classifier

Here's a simple image classifier implemented in TensorFlow Keras (right click to open in new tab): https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/quickstart/...
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2answers
149 views

How to express accuracy of a regression ANN that uses MSE loss function?

I have a regression MLP network with all input values between 0 and 1, and am using MSE for the loss function. The minimum MSE over the validation sample set comes to 0.019. So how to express the '...
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1answer
45 views

How many hidden layers are needed for this training data set

I'm trying to separate classes in 3D space, the data are as in the sketch below: There are 3 classes: 0,1,2; and with the look into the sketch, it seems that I need 3 planes to separate the classes, ...
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0answers
22 views

Can two neural networks be better instead of one with a categorical feature?

Let's assume, that I have a neural network with few numerical features and one binary categorical feature. The network in this case is used for regression. I wonder if such a neural network can ...
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0answers
22 views

Confidence Maps and Non-Linearity

I am currently trying to improve a CNN architecture that was proposed for generating depth images. The architecture was originally proposed for autonomous driving and it looks like following : The ...
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1answer
47 views

Have neural networks something to offer which goes beyond regression analysis?

Neural networks are perceived as a powerful regression tool. If a dataset contains of input/output relations, the neural network can adjust it's internal parameters to interpolate the missing data. In ...
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1answer
114 views

Can supervised learning be recast as reinforcement learning problem?

Let's assume that there is a sequence of pairs $(x_i, y_i), (x_{i+1}, y_{i+1}), \dots$ of observations and corresponding labels. Let's also assume that the $x$ is considered as independent variable ...
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0answers
85 views

Polynomial Regressor vs Neural Network Regressor

So as far as my knowledge (might be a bit vague and not mathematical) goes a Neural Network can and should only be able to approximate a bounded function, which is not the case of a Polynomial ...
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22 views

Literature on Sequence Regresssion

I have some rated time-sequential data and I would like to test if an ANN can learn a correlation between my measurements and ratings. I suspect I could just try a CNN where 1 Dimension is time or an ...
2
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0answers
94 views

What are the possible neural network architecture for linear regression or time series regression?

I started modeling a linear regression problem using dense layers (layers.dense), which works fine. I am really excited, and now I am trying to model a time series linear regression problem using CNN, ...
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1answer
58 views

What is the relationship between degrees of freedom and the size of the training dataset?

I am going through the book Pattern Recognition by Bishop. At one point he says For $M = 9$, the training set error goes to zero, as we might expect because this polynomial contains 10 degrees of ...
2
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1answer
15 views

Should I model a problem with quantised output as classification or regression?

Say I have some data I am trying to learn, and I'm aware that the output is quantised in some way, e.g. I can get only get discrete values (0.1, 0.2, 0.3...0.9) in a finite range. Would you treat ...