Questions tagged [regression]
Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.
1,585 questions
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What does the Grenander condition imply about the data-generating process of $(y_i, x_i)$?
Consider a correctly specified linear model $$ y_i = x_i^\top \beta + \varepsilon_i,\quad i=1,\dots,n, $$ where the errors $\varepsilon_i$ are independent with zero mean and finite variance. ...
- 101
5votes
1answer
28views
Wind Power Data Analysis - Python
I am seeking some help and or perspectives in solving a problem. I have a dataset (accessible here) with the following columns: DATE: this is the date in dd/mm/yyyy format HH: this is the "half-...
3votes
1answer
55views
Why MAE is hard to optimize?
In numerous sources it is said that MAE has a disadvantage of not being differentiable a zero hence it has problems with gradient-based optimization methods. However I've never saw an explanation why ...
- 163
5votes
1answer
49views
How to do Exploratory Data Analysis when my response variable is binary?
I am doing a multilevel regression, and my response variable is binary (presence of females on a tech board). all the EDA methods i know are about plotting correlation, but this as this is a binary i ...
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Reverse engineering what stocks are in a dummy ETF using regression (lasso, ridge, etc) in Python
I'm trying to reverse engineer what stocks are in a ETF using python. In my code, I create a fake ETF that is equal weighted 20 random stocks. I then try to reverse engineer whats in my ETF using ...
- 29
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Performing piecewise polynomial regression on the Auto dataset (ISLR2)
I am trying to analyze some data from the Auto data set (from ISLR2). I am trying to fit a piecewise polynomial to the acceleration column. But every time I run my code, R throws this error: ...
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Modeling Continuous Variable Given Only Categorical Dependent Variables
In my scenario, we have several categorical variables with multiple levels as predictors (X), and a continuous response variable (y). We have many observations of Y for every possible combination of ...
2votes
1answer
53views
Is a two-phase model (ensembling/stacking) a valid approach for forecasting product demand?
I am working on a project to forecast food sales for a corporate restaurant. Sales are heavily influenced by the number of guests per day, along with other factors like seasonality, weather conditions,...
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CNN for gaze regression predicts near the mean
I am currently building my first CNN network on my own for a regression task for which the network must predict the coordinates I am looking at on my screen based on an input image taken through my ...
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Data Exploration - Uneven Sampling Frequency
I apologize in advance for the noob question, this is the first ML project that I have attempted although I have some stats background. I am in the data exploration phase for a project, where I am ...
1vote
1answer
48views
I didn't scale all features I used for prediction, does it make sense?
In my regression-based machine learning project, I have features like coordinates (latitude and longitude) that I prefer not to scale or transform. The main reason is that reversing the transformation ...
- 103
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Can a time series model self correct during prediction?
Can a time series model that makes predictions for January to December and let's say in March the actual data's value in above the 95% confidence interval prediction change weights dynamically and ...
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Regression model R2 drops when I remove outliers: is that even possible?
I'm analyzing how outliers in my dataset of size 8x8000 affect regression models. I have three scenarios: raw dataset (with outliers), Winsorized dataset (2% of the extreme outliers adjusted), and ...
- 103
3votes
1answer
296views
How to train a model to estimate the coefficients of a coupled ODE?
Consider the coupled ODE system below (Lotka-Volterra equations): $$ \frac{dx}{dt} = \alpha x - \beta x y, \\ \frac{dy}{dt} = - \gamma y + \delta x y , $$ How can I train a model to estimate the ...
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1vote
3answers
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What is the impact of low correlation on regression and classification problems, and how does it affect model performance?
I’m building two models (one for a regression problem and the other for a classification task) but I am facing low correlation in the data (lower in the classification problem than in the regression ...
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