Questions tagged [regression]

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

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Which dataset for multivariate time series forecasting

I'm trying to forecast Real estate Price , it's not a prédiction. But a forecast Like the Price of a an appartement in 2023 or 2024, i'm asking about how should be my dataset ? Can I use a dataset ...
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ValueError: continuous is not supported

I am working on a regression problem and building a model using Random Forest Regressor but while trying to get the accuracy I am getting ValueError: continuous is not supported. ...
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How to compute threshold?

I would like to detect anomalies for univariate time series data. All examples on internet show that, after you predict the model, you calculate a threshold for the training data and a MAE test loss ...
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Predicting a signal based on other signals

I want to predict a signal based on other related signals, how would I go about doing this? My current approach is to do some feature extraction (in the time and frequency domain) on both the ground ...
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the error value of two model is different, one has smallest MAE but another one have smallest MSE

I have two machine learning model, the model result different error value in MAE and MSE. M1 have smallest MAE, but M2 have smallest MSE. Can anyone explain to me, why this happen? I also add my ...
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Machine Learning for conditional density estimation

Suppose I have a set of examples $X = (x_1,x_2,..,x_n)$ with continuous numeric targets $Y = (y_1,y_2,..,y_n)$. While it is standard to use regression models to make point predictions of $y_i$ as $f(...
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Task of regression on graphs

Which tools are available to extract features from a graph. After that, I would like to perform regressions on those features. Initially, I was thinking about using the adjacency matrix of the graph. ...
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Scaling and handling highly correlated features in tabular data for regression

I am working on a regression problem trying to predict a target variable with seven predictor variables. I have a tabular dataset of 1400 rows. Before delving into the machine learning to build a ...
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Get negative predicted value in Support Vector Regresion (SVR)

I am doing Covid-19 cases prediction using SVR, and getting negative values, while there should be no number of Covid-9 cases negative. Feature input that I was used is mobility factor (where have ...
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Do you know any R library to do utility-based regression?

I wanted to know if anyone knew of a library that has utility based regression implemented or someone who knows how to adapt normal regression to utility based regression. I can use either R or python ...
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What would be the best method for uncertainty analysis for a CNN-based regression model?

I am trying to establish a relationship between a dependent variable and 18 independent variables using a CNN-based regression model. What would be the best method for uncertainty analysis? Thanks.
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What is the best method to determine variable importance in a CNN-based regression model?

I am trying to establish a relationship between a dependent variable and 18 independent variables using a CNN-based regression model. What would be the best method to determine variable importance in ...
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I am trying to establish a relationship b/n hcanopy & 18 predictors (vv_name & vh_name) using CNN but my model isn't learning. How can I resolve it?

Before building and running the model, I have rescaled and normalized the data. Here is my model - ...
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Interpreting Homoscedasticity and Residual spread for linear regression

I am new to Data analytics. I have difficulties of understanding what both the Homoscedasticity and residual histogram is trying to convey. Please any help is appreciated. I have a linear regression ...
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can someone explain how to create new features using feature interactions?

There is this notebook solving housing prices. https://www.kaggle.com/code/jesucristo/1-house-prices-solution-top-1/notebook?scriptVersionId=12846740 and it had this bit of code, can anyone explain ...
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How to choose parametric regression model with two predictors

I have a response variable (depicted on the y-axis in the following plots), that I want to regress using two predictors. On each plot, one predictor is along the x-axis, the other is color-coded in ...
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How to interpret a linear regression effects graph?

could someone tell me how to interpret the following graph? It corresponds to a graph in which the effects of the variables in a linear regression are observed, but its interpretation is not clear to ...
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Why should MLE be considered in Logistic Regression when it cannot give a definite solution?

If MLE (Maximum Likelihood Estimation) cannot give a proper closed-form solution for the parameters in Logistic Regression, why is this method discussed so much? Why not just stick to Gradient Descent ...
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Classification or Regression approach?

I have a dataset with x variables and the target y (between 0 and 100%, so 0 and 1) My goal os to predict if a sample is in a group of y [0,0.25), [25,50) or [50,100]. And I am wondering if I should ...
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Poor neural net regressive fit to data that exhibit clear structure

I've been trying to use a simple NN to model data I've generated. The data lack a closed form expression, but exhibit clear structure. The MWE below emulates similar data. I find that any NN I create, ...
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VIF Vs Mutual Info

I was searching for the best ways for feature selection in a regression problem & came across a post suggesting mutual info for regression, I tried the same on boston data set. The results were as ...
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Why is my validation loss never INcreasing?

I am currently training different neural networks for the binary classification of images. When using the logistic regression, my validation loss never increases, even not after 5000 epochs. I thought ...
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Real World Regression R & p-value

I am trying to figure out whether our customer support has an impact on tickets opened by customers. Our employees should contact customers to avoid that a user will open a ticket. The data is quite ...
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Can MARS regression be used for classification?

I am dealing with a data set in which I have to classify between a diseased and a non-diseased individual. I was wondering if it is possible to adapt the MARS regression (Multivariate adaptive ...
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How to tackle imbalanced regression?

I've recently encountered a problem where I want to fit a regression model on data that's target variable is like 75% zeroes, and the rest is a continuous variable. This makes it a regression problem, ...
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Line in logistic regression

In Logistic Regression, do we fit a straight line through the data such that maximum number of points lie on the line, or we fit a line such that the 2 classes are separated with the line in between?
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Loss stuck for regression model

I'm training a model that returns 2 parameters. These two parameters are used for classical image processing: a threshold for the kirsch-operator the number of iterations for billateral filter. The ...
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Predicting using previous data

I am doing an experiment using ultrasonic radar which rotates 180 degrees clockwise and anti-clockwise. When the sensor encounters an obstacle in front, the algorithm should determine which direction (...
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Which statistical analysis to use when you assume non-linear model but not-specified?

I'm a psychology student/researcher and looking to model a phenomenun in which there are 3 variables. The relation of these variables are exactly unclear but I assume these variables are non-linear in ...
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Regression trees with partykit

I am using the partykit library in order to use regression trees to make predictions. My goal is to create a regression tree that uses binomial as the distribution family parameter. I have been going ...
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Regression - random forest vs Fully connected neural net

I am running a regression on the following data set to predict white wine quality Data set link: https://archive.ics.uci.edu/ml/datasets/wine+quality Data csv name: winequality-white.csv Features: 1 - ...
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Multivariate multiple input batch data regression

So, in general, my question is how to approach modeling with this kind of data. I'll try to explain the challenge and some of my thought process. Challenge I have batches of cars sold. One batch may ...
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How to incorporate predictor variable without future information into a model?

I will use an extremely simplified example to ilustrate the question, but I think the answer shsould hold for more generalised cases. Let's say I want to create a time series regression model (the ...
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regression.fit(x_train, y_train) is not working on python

I try to deal with my homework. The Job is to take the Achievement data and perform a multi-linear regression on it. The code is published here. I am quite new to programming in Python and in data ...
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KFold splitting of time series

I have a fundamental question about train/test split for time series. Let me give a simple example to illustrate my question, which is actually related to a more complex problem. Example Suppose I ...
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Finding contribution/weight of features in output from a series of data

I have a dataset which consists of the following features : a1,a2,.....aN.... sum of all the features together gives a constant output say 100. For eample the dataset may look like this. a1 a2 ...
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What is the good way to print classifier lines with sklear learn LinearSVC

I've tried to make a multivariate regression with LinearSVC and I have seen two ways to print the lines of the classifier, and they haven't the same output. I have seen one on this forum and the ...
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What is the difference between keras tuned hyperparameters and manually defined Sequential model with same hyperparameters?

I have a dataset that I divided into 10 splits of training, validation and test sets for a regression problem. I used the first split and RandomSearch in ...
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regression model behaves (predicts) like classification

I have a simple data: ...
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Are there any known methods to generalize multiple trajectories into one "optimal" path based on energy consumption?

Say that I have a database with timeseries coordinate data from a vehicle going from A to B multiple times but with slightly varying trajectories each time, leading to different amounts of energy ...
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How to build regression model on residuals

Let's say you have a good-performing regression model, but the end result is not just yet there. One of the ideas, I came across to improve model performance is to build a second model using the first ...
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Is There Techniques for creating synthetic Data for Regression Problem i tried SMOTE and its variant but these are for classification problem

This is my data "Volume" is my Target variable and all other are Independent variables i just applied labelencoder on Area_categ , wind_direction_labelencod and on current _label_encode and ...
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How can I do continuum regression in R?

I am looking for a R package that does continuum regression. More concrete I need a function that does continuum regression s.t. I can evaluate the values afterwards. At least extracting MSE or RMSE ...
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Multivariate data preprocessing

I am trying to understand how multivariate data preprocessing works but there are some questions in my mind. For example, I can do data smoothing, transformation (box-cox, differentiation), noise ...
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Difference between scaling just x or x and y in PCA / principle component regresseion

Before doing principle component regression it is important to scale the data. But which data exactly? Is it enough if I just scale X or do I have to scale the whole data set, containing X and Y (=...
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Searching machine learning algorithm for regression problem with many features

I have a machine learning problem with about 160 features and 400 cases and I want to find the best predictors for a continuous outcome. The dataset contains variables of psychotherapists and clients. ...
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How to estimate a best fitting line that separates variables on a scatterplot by some third binary outcome variable with 95% accuracy?

0 I've been thinking about this and haven't found a non-brute force method of doing it. I have a series of scatterplots depicting the relationship between two predictor variables and a third binary ...
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How to perform linear regression on a parameter that represents state/configuration of a machinery in a production process?

I am trying to perform linear regression on a manufacturing process in order to determine the influencing parameters on a particular product. The thing is there are several production parameters, and ...
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Why is the L2 penalty squared but the L1 penalty isn't in elastic-net regression?

There was some data set I worked with which I wanted to solve non negative least squares (NNLS) on and I wanted a sparse model. After a bit of experiementing I found that what worked the best for me ...
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Making a regression problem into a classification followed by regression problem

I have a dataset where the output value ranges from 0-600(greater than 600 is possible but is not present in my dataset). But the number of 0's in my output variable is close to 50%. I've converted it ...
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