# Questions tagged [regression]

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

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### Why do we need to discard one dummy variable?

I have learned that, for creating a regression model, we have to take care of categorical variables by converting them into dummy variables. As an example, if, in our data set, there is a variable ...
3k views

### Solving a system of equations with sparse data

I am attempting to solve a set of equations which has 40 independent variables (x1, ..., x40) and one dependent variable (y). The total number of equations (number of rows) is ~300, and I want to ...
353 views

### What is the difference between classification and regression?

I understand classification....a discrete response or category, like animal is dog or cat. The author says..."Regression techniques predict continuous changes such as the change in temperature, power ...
21k views

### Validation loss is not decreasing

I am trying to train a LSTM model. Is this model suffering from overfitting? Here is train and validation loss graph:
82 views

### Regression Algorithms in Production

I am interested in predicting if a doctor would prescribe a specific drug and have chosen Logistic Regression as a starting point. I have a few questions: Is feature selection the first step to take ...
36k views

### Neural Network for Multiple Output Regression

I have a dataset containing 34 input columns and 8 output columns. One way to solve the problem is to take the 34 inputs and build individual regression model for each output column. I am wondering ...
13k views

### Why do we convert skewed data into a normal distribution

I was going through a solution of the Housing prices competition on Kaggle (Human Analog's Kernel on House Prices: Advance Regression Techniques) and came across this part: ...
26k views

### What does “linear in parameters” mean?

The model of linear regression is linear in parameters. What does this actually mean?
4k views

### Is there a library that would perform segmented linear regression in python?

There is a package named segmented in R. Is there a similar package in python?
4k views

### MAD vs RMSE vs MAE vs MSLE vs R²: When to use which?

In regression problems, you can use various different metrics to check how well your model is doing: Mean Absolute Deviation (MAD): In $[0, \infty)$, the smaller the better Root Mean Squared Error (...
9k views

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703 views

### Quasi-categorical variables - any ideas?

Let's say I'm trying to predict a person's electricity consumption, using the time of day as a predictor (hours 00-23), and further assume I have a hefty but finite amount of historical measurements. ...
7k views

### Neural network with flexible number of inputs?

Is it possible to create a neural network which provides a consistent output given that the input can be in different length vectors? I am currently in a situation where I have sampled a lot of audio ...
1k views

### How does Implicit Quantile-Regression Network (IQN) differ from QR-DQN?

For several months I browsed the internet hoping to find a user-friendly explanation of the Implicit Quantile Regression Network (IQN). But, it seems there is none at all. How does IQN differ from ...
2k views

### Modelling Unevenly Spaced Time Series

I have a continuous variable, sampled over a period of a year at irregular intervals. Some days have more than one observation per hour, while other periods have nothing for days. This makes it ...
1k views

### Fitting lines through large point clouds

I have a large set of points (order of 10k points) formed by particle tracks (movement in the xy plane in time filmed by a camera, so 3d - 256x256px and ca 3k frames in my example set) and noise. ...
20k views

### Xgboost - How to use feature_importances_ with XGBRegressor()?

How could we get feature_importances when we are performing regression with XGBRegressor()? There is something like ...
1k views

131 views

### R-square or adjusted R-square for one variable model?

I have model like y=mx. Since the adjusted R2 tells you the percentage of variation explained by only the independent variables that actually affect the dependent variable and I have only one ...
2k views

### Prediction after one hot encoding

I have a regression model that I want to make prediction based on values that I will get from an end user. In my dataset, I have one categorical variable region ...
51 views

### Noise has 0 mean

Why do we assume that in a dataset the error, presented as the random error term, has mean 0 ? To me seems impossible that every event we can study in everyday life has an error with 0 mean...