Questions tagged [regression]
For questions related to regression (both linear and non-linear) in the context of machine learning and AI.
84
questions
0
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0answers
13 views
Can transformers be used to improve regression?
I was recently reading a bit about transformers and I don't understand them very much but I was wondering if anyone knows if any of their techniques such as attention mechanism or anything has been ...
0
votes
1answer
39 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 $X[i, :]=[0,...,1,...,0,x_{i}]$. ...
3
votes
1answer
111 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, ...
0
votes
0answers
17 views
Converting inputs as a batch for time series classification would increase accuracy?
I have sensor dataset. I have already classified these data with LSTMs.I have a dataframe with 2 features and a class column. Assume that I take every two rows(inputs) respectively and make the ...
3
votes
2answers
44 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 ...
0
votes
0answers
15 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 ...
0
votes
1answer
19 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 ...
3
votes
2answers
97 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 ...
0
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0answers
27 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 ...
1
vote
1answer
53 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 ...
1
vote
1answer
73 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 ...
0
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1answer
43 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:
...
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0answers
13 views
How to use MultiTarget Regression without classes
I would like to forecast a dataset composed of two attributes, a sample is displayed below:
...
0
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0answers
27 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 ...
2
votes
1answer
110 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 ...
0
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0answers
105 views
Remove drawbacks of Neural Network regressor as compared Polynomial Regressor
As far as my knowledge goes (might be a bit vague and not mathematical), a Neural Network can and should only be able to approximate a bounded function, which is not the case of a Polynomial Regressor....
0
votes
1answer
51 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 ...
3
votes
1answer
72 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?
0
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1answer
94 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 ...
1
vote
0answers
35 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 ...
2
votes
2answers
311 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 '...
-1
votes
1answer
55 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 ...
0
votes
2answers
64 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 ...
2
votes
0answers
28 views
Can neuro-fuzzy systems be used for supervised learning tasks with tabular data?
Is it possible to use neuro-fuzzy systems for problems where ANNs are currently being used, for instance, when you have tabular data for regression or classification tasks? What kind of advantage can ...
0
votes
1answer
90 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 ...
2
votes
1answer
42 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 ...
0
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0answers
22 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 ...
12
votes
7answers
14k views
How to classify data which is spiral in shape?
I have been messing around in tensorflow playground. One of the input data sets is a spiral. No matter what input parameters I choose, no matter how wide and deep the neural network I make, I cannot ...
1
vote
0answers
21 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 ...
7
votes
3answers
464 views
Which predictive algorithm can be used to predict a number given other numbers?
I am currently searching for a supervised learning algorithm that can be used to predict the output given a large enough training set.
Here's a simple example. Suppose the training dataset is ...
1
vote
0answers
28 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
93 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 ...
3
votes
4answers
128 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)....
0
votes
1answer
50 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
15 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
0answers
31 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
0answers
34 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
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0answers
68 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
59 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 ...
5
votes
1answer
180 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 ...
2
votes
0answers
61 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 ...
0
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0answers
18 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
102 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
31 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
52 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
47 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 ...
5
votes
2answers
1k views
Should the prediction of the body temperature given a camera image be modelled as classification or regression?
I am fairly new to deep learning in general and I am currently facing a problem I want to solve using neural networks and I am unsure if it is a classification or regression problem. I am aware that ...
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
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0answers
39 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 ...
