Questions tagged [regression]

Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.

462 questions with no upvoted or accepted answers
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votes
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Loss Function for Probability Regression

I am trying to predict a probability with a neural network, but having trouble figuring out which loss function is best. Cross entropy was my first thought, but other resources always talk about it in ...
8
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2answers
2k views

Forecasting Multiple (few hundreds) uni-variate time series with inflated zeros

I am a novice seeking help to gain experience in Data Science. Let us take a scenario where a big company would like to forecast its sales (a specific product) across different stores in different ...
6
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1answer
261 views

When is the sum of models the model of the sum?

The response variable in a regression problem, $Y$, is modeled using a data matrix $X$. In notation, this means: $Y$ ~ $X$ However, $Y$ can be separated out into different components that can be ...
5
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1answer
66 views

Multi-target regression tree with additional constraint

I have a regression problem where I need to predict three dependent variables ($y$) based on a set of independent variables ($x$): $$ (y_1,y_2,y_3) = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \dots + \...
5
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2answers
170 views

Evaluation of regression models with different evaluations (MSE, variance, VAF etc.)

When comparing several regression models in terms of quality, it seems like most have agreed on the MSE. There are also papers comparing "variance" and "variance accounted for (VAF)&...
5
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1answer
114 views

Predicting change of shapes/coordinates

I'm trying to find a way to predict/calculate how a shape (e.g. outline of a glacier) will change in the future—based on its history (previous shape) and additional factors (e.g. Δtemperature). In my ...
5
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1answer
1k views

In XGBoost, how to change eval function and keeping same objective?

I would like to keep the objective as "reg:linear" and eval_metric as customized RMSE as follows: ...
4
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1answer
153 views

Linear Regression bad results after log transformation

I have a dataset that has the following columns: The variable I'm trying to predict is "rent". My dataset looks a lot similar to what happens in this notebook. I tried to normalize the rent ...
4
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1answer
67 views

What ML architecture fits fixed length signal regression?

My problem is of regression type - How to estimate a fish weight using a fixed-length signal (80 data points) of the change in resistance when the fish swim through a gate with electrodes (basically 4 ...
4
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1answer
40 views

Spatially constrained geospatial similarity

What's the current methodology for clustering geospatial data by features? Example: I have some demographic dataset. Let's say this contains average home price and population density. So, an example ...
4
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1answer
145 views

How to incorporate new features in an existing machine learning model?

Suppose we have trained a regression model $M$ on a fixed set of $n$ features, $F_1,F_2,…,F_n$ on a particular dataset $G$. Now assume that after model training, additional features ($F_{n+1},…$) ...
4
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1answer
115 views

In a residuals vs fitted plot, how do I interpret a homoscedastic variance that is not randomly distributed above/below the line?

I'm learning linear regression, and I ran a step function for linear regression and checked out the residuals vs fitted plot for the final equation. The residual looks homoscedastic but it's not ...
4
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4answers
332 views

What kind of regression model should I do?

my research question is the examine the effect of "receiving attention" from other members in an online community on "sustained participation" on the website. I decided to measure "sustained ...
3
votes
1answer
102 views

Neural Network regression negative performance

I have a problem with the performance of a multi layer perceptron regressor (neural network) and I cannot figure out why. Task: I am trying to improve a time series prediction. I have predictions of a ...
3
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1answer
64 views

Chossing between regression models

This is the first time I attempt to use machine learning with Keras. In contrast to others I need to use one of the disadvantage of such algorithms. I need a ...
3
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2answers
77 views

Continuous Estimated Time of Arrival

I'm trying to create a model for when a shipped product will arrive at its destination. There are several stages the delivery goes through, so it's not just drive time from point A to point B. My ...
3
votes
1answer
80 views

Physical modelling with neural networks - single output + stack ensemble vs multi-output

We are trying to replace an existing physical model (8 inputs/7 outputs) with artificial neural networks. The physics behind the existing model is mainly thermodynamics of humid air for air ...
3
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1answer
106 views

Transforming negative correlated non linear variable to linear positive correlated variable

At my office, I am stuck in a weird situation. I am asked to perform a regression algorithm on the data, in which the target variable is continuous having values range between 0.6 to 0.9 with 8 digits ...
3
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1answer
403 views

Discriminator of a Conditional GAN with continuous labels

OK, let's say we have well-labeled images with non-discrete labels such as brightness or size or something and we want to generate images based on it. If it were done with a discrete label it could ...
3
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1answer
847 views

Linear Regression on data with bimodal outcome

I have a data set with 3,000 features and continuous dependent variables of time with 18,000 instances. The histogram of the dependent variables show that the they have a bimodal distribution. I am ...
3
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0answers
44 views

How to make machine learning model that reports ambiguity of the input?

Suppose I want to build a neural network regression model that takes one input and return one output. Here's the training data: ...
3
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0answers
1k views

How to train continuous/soft classification model?

The classic classification problem is like finding the function $F:\mathbb{R}^n\mapsto \{0,1\}$. The label set will be [Apple,Banana,Banana,...,Apple]. What if I want to train a function $F:\mathbb{R}...
3
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0answers
322 views

Genarate one hour time interval array using pandas in python (import from csv) to predict next value

I am trying to generate one hour one hour time interval to predict next value according to my data set imported from csv file. Here according to the time it will give outputs include in x column. This ...
3
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1answer
67 views

Preventing fitting Regression CNN to the mean when dataset has only few outliers

I am trying to train a CNN for regression on a dataset where most of the points lie around a similar output value. There are however a few outliers that are very important but they are less ...
3
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2answers
1k views

Piece-wise regression by clustering

I was wondering about the possibilities of clustering numerical data (more than 3 dimensions) into different clusters and doing curve fitting on each cluster to get much higher accuracy than using a ...
3
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0answers
744 views

Keras custom loss - operation on additional data

I am trying to create a custom loss function for a Keras regression task. I am predicting the points scored per minute in a game, and training on "matches" of variable lengths, in minutes. In an ...
3
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1answer
173 views

Is decision tree regression comparable to locally weighted regression

I am new to decision tree method. For decision tree regression model, does it just fit a piece wise step function over data? When and why would people prefer it over some traditional regression like ...
3
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3answers
90 views

Find recurrent dates in a small set (and get rid of non recurrent ones)

I need help in the analyse of a categorization problem. Given a set of dates (small set: 20 elements maximum), I would like to group dates which are equally distributed (with a tolerance). It can be,...
3
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1answer
44 views

Am I supposed to be using Mixed Effects?

I ran a GLS random effects regression on some NBA data in Stata, and I was told that it was wrong because I didn't use mixed effects model. This may every well be the case, but I was quite confused by ...
3
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1answer
2k views

Why is Spark's LinearRegressionWithSGD very slow locally?

I have been trying to run linear regression with SGD that is found in Spark mllib for some time and I am experiencing huge performance problems. All examples that I was looking have number of ...
2
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0answers
25 views

change target variable value to reflect better affordability

Context I am working on a regression problem trying to predict affordability. My dataset contains daily installments repaying a purchase in a form of contract. Essentially, a minimum daily rate the ...
2
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0answers
21 views

(R) can I convert a categorical variable into a numeric equivalent in linear regression to predict a continuous variable?

Specifically, I have an item code as one of the independent variables that can have several hundred possible values results in underfitting when predicting the projected availability of that item. I'd ...
2
votes
1answer
34 views

Can I compare two models trained on different but similar datasets to help find differences between the two datasets?

I have a multivariate dataset the contains A and B. I want to see if there are differences between the A and B samples. I currently have two ideas on how to do this, but I am not sure if they are ...
2
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0answers
26 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 $x_1, y_1, x_2, y_2, class, depth$. I calculated this depth (of each ...
2
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1answer
11 views

How to build predictive/multivariate (and uni, bi, etc.) regression model for categorical dataset that is already one hot encoded?

I'm a bit confused about how to build any kind of ML model with only categorical data. I have a dataset of training completed by each person. The dataset has about 25 columns (names of the training) ...
2
votes
1answer
32 views

Discouraging values or smoothing out results when model fitting

I'm working on training a network to do direction of arrival prediction and I'm having the issue that no matter what my network is (ResNet 18 - 101, CRNN, CNN, etc...) my results tend toward one small ...
2
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0answers
13 views

Multiple regression with non-normal data in minitab - help

I am aiming to assess the effect of BMI (continuous) on certain biomarkers (also continuous) whilst adjusting for several relevant variables (mixed categorical and continuous) using multiple ...
2
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1answer
24 views

Given a regression based model with many feature variables; what tools would you utilize to figure out which feature variables add the most variance?

Given a hypothetical dataset {S} with 100 X feature variables and 10 predicted Y variables. X1 ... X100 Y1 .... Y10 1 .. 2 3 .. 4 4 .. 3 2 .. 1 Let's say I want to improve the accuracy of Y1. I am ...
2
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0answers
48 views

Time series forecasting. How use future values

I have a time series dataset containing hourly data from a few year, like below. Let's assume that I want to make prediction for the next 3 hours (2021-01-01 19:00, 2021-01-01 20:00, 2021-01-01 21:00)....
2
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0answers
48 views

How to process categorical variable having lots of unique values in linear regression?

I have House Price dataset and I am using linear regression to predict the house price. while data preprocessing I found a variable called "Location" and it have around 342 unique value. For ...
2
votes
1answer
65 views

How to approach different image resolutions in deep learning for regression problem?

I have an image dataset of various resolutions and using regression DNN model with fixed n*n input resolution. As model learns certain positions in the image, I've been using zero padding to fit ...
2
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0answers
111 views

How to Ensemble LGBM and XGBoost Machine Learning Models?

I want to Ensemble my predictions for the StratifiedKFold of LGBM and XGBoost into another LGBM Model. I had written the following code which works when the data set has an ID_COL, but in this data ...
2
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0answers
21 views

Deriving vectorized form of linear regression

We first have the weights of a D dimensional vector $w$ and a D dimensional predictor vector $x$, which are all indexed by $j$. There are $N$ observations, all D dimensional. $t$ is our targets, i.e, ...
2
votes
1answer
47 views

Is there any feature selection method specific for regression analysis?

Is there any feature selection method that works especially well for regressions? I used backwards elimination and forward selection before a lot but I've recently read that even though it's ...
2
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0answers
20 views

interpolation - graphical quality evaluation

I try to compare different interpolation models quality and I'm looking for a graphical tool to do that. Application case: I'm not familiar with intepolation using neural networks. I decide to test it ...
2
votes
1answer
141 views

1st order Taylor Series derivative calculation for autoregressive model

I wrote a blog post where I calculated the Taylor Series of an autoregressive function. It is not strictly the Taylor Series, but some variant (I guess). I'm mostly concerned about whether the ...
2
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0answers
53 views

Regression dataset with categorical features

I have thought of a regression technique that I want to try on several datasets. I would like these datasets to have the following properties: Be a tabular dataset (no images). Have at least 20k rows,...
2
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1answer
39 views

Handling near duplicate observations in a regression / Bayesian model

I am working on a model where the underlying data is inherently correlated by groups. So some of my observations are almost duplicates but not quite. The problem is pretty simple, I have a y ...
2
votes
2answers
254 views

Which loss function is the best loss function when using XGB regression with highly skewed dataset?

Which loss function is the best loss function when using XGB regression with a highly skewed dataset? The skewness of the data is very high. I used XGBoost with objective function of linear ...
2
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0answers
20 views

Selecting Regressors from list for time series

I have been try to get results from a time series problem and I have used fb's prophet algo for that. Now problem is I have to select comb of those regressors giving me least rmse. I used granger ...

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