Questions tagged [regression]
For questions related to regression (both linear and non-linear) in the context of machine learning and AI.
67
questions
1
vote
0answers
20 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 ...
1
vote
1answer
80 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 ...
0
votes
0answers
12 views
Multiple Time Series (multivariate) Regression
I am trying to do regression using data from multiple time-series.
From what I see some tutorials propose to stack time-series data one into another in a single row of features.
I looks to me ...
0
votes
1answer
42 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 ...
0
votes
1answer
39 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
0answers
9 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 ...
0
votes
1answer
23 views
Wind speed forecasting using ARIMA model in Python3
Recently, I started working on time-series models and would mention that I am very new to python and ML as a whole.
I tried to implement a time-series model on wind speed data. Being a newbie, I ...
0
votes
0answers
29 views
Noise reduction in output from regression network
I am building a regression network to predict gravity strength based on meteorological data. I'm getting a fairly good fit, but quite a bit of noise in my output. I can find a lot of info on how to ...
0
votes
1answer
39 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 ...
0
votes
0answers
31 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. ...
0
votes
0answers
43 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: ...
0
votes
0answers
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 ...
0
votes
0answers
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 ...
2
votes
0answers
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 ...
2
votes
0answers
42 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 ...
2
votes
0answers
54 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 ...
1
vote
0answers
28 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 ...
1
vote
0answers
36 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 ...
1
vote
0answers
42 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 ...
2
votes
0answers
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 ...
0
votes
0answers
36 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 ...
1
vote
0answers
52 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 ...
2
votes
0answers
28 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.
...
2
votes
3answers
139 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 ...
3
votes
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 ...
3
votes
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 ...
3
votes
2answers
102 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 ...
1
vote
1answer
47 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 ...
2
votes
1answer
42 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 ...
1
vote
0answers
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 [...
3
votes
1answer
75 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 ...
1
vote
1answer
71 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 ...
2
votes
1answer
35 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 ...
2
votes
1answer
80 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 ...
3
votes
4answers
105 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)....
1
vote
0answers
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 ...
1
vote
0answers
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 ...
2
votes
0answers
22 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 ...
3
votes
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 ...
2
votes
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(...
2
votes
1answer
67 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
votes
1answer
72 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 ...
3
votes
1answer
84 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, ...
4
votes
1answer
86 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 (...
0
votes
2answers
189 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
votes
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 ...
0
votes
1answer
61 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/...
2
votes
2answers
180 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 '...
0
votes
1answer
55 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, ...
2
votes
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 ...
