Questions tagged [regression]
For questions related to regression (both linear and non-linear) in the context of machine learning and AI.
93
questions
1
vote
0answers
11 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
...
0
votes
0answers
14 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 ...
0
votes
0answers
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 ...
0
votes
1answer
30 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 ...
0
votes
0answers
11 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 ...
0
votes
0answers
7 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 ...
0
votes
0answers
23 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/...
0
votes
0answers
20 views
Image regression - estimating sensors from images
I am trying to use images to predict the sensor data of a racing game.
Being a bit of a newcomer I have multiple questions. All help/suggestion is appreciated.
Dataset
The dataset looks something like:...
0
votes
1answer
43 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 [...
0
votes
0answers
22 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 ...
-2
votes
1answer
54 views
Assumptions of a Linear Regression [closed]
I was going through the concept of Linear Regression and ran into the concept of deciding whether a Linear Regression Model is the best fit for your data by 5 assumptions:
Linearity
Homoscedasticity
...
0
votes
0answers
16 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
42 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 $X[i, :]=[0,...,1,...,0,x_{i}]$. ...
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 ...
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 ...
3
votes
2answers
64 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 ...
2
votes
1answer
101 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 ...
0
votes
0answers
32 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 ...
0
votes
0answers
15 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
votes
1answer
73 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:
...
0
votes
0answers
30 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
476 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 ...
3
votes
1answer
80 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?
1
vote
1answer
37 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 ...
1
vote
0answers
46 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 ...
-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 ...
2
votes
1answer
45 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
votes
0answers
33 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
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 ...
1
vote
0answers
30 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
98 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
1answer
57 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
58 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
19 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
172 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
33 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
2answers
80 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
99 views
GPU-training on Google Cloud Platform slower than CPU-training [closed]
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
60 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 ...
2
votes
0answers
30 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
67 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
131 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
74 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
51 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
33 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
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 ...
1
vote
0answers
62 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
31 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
176 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 ...
