# Questions tagged [regression]

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

976 questions
Filter by
Sorted by
Tagged with
35k 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: ...
11k views

### 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 ...
26k views

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

The model of linear regression is linear in parameters. What does this actually mean?
47k views

### How can I check the correlation between features and target variable?

I am trying to build a Regression model and I am looking for a way to check whether there's any correlation between features and target variables? This is my ...
1k views

### Airline Fares - What analysis should be used to detect competitive price-setting behavior and price correlations?

I want to investigate price-setting behavior of airlines -- specifically how airlines react to competitors pricing. As I would say my knowledge about more complex analysis is quite limited I've done ...
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 ...
20k views

### What does “baseline” mean in the context of machine learning?

What does "baseline" mean in the context of machine learning and data science? Someone wrote me: Hint: An appropriate baseline will give an RMSE of approximately 200. I don't get this. Does he ...
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 ...
1k views

### What regression to use to calculate the result of election in a multiparty system?

I want to make a prediction for the result of the parliamentary elections. My output will be the % each party receives. There is more than 2 parties so logistic regression is not a viable option. I ...
1k views

1k views

### What are some resources to test your data science skills?

I can't get a job to save my life so I am guessing my lack of skills is an issue. I've been doing a lot of reading on statistics and I am getting antsy - I want to move from theory to application and ...
1k views

### Improve a regression model and feature selection

I am working on Azure ML Studio and try to create a regression model to predict a numerical value. I will try to describe my features and what I have done until now. My data with about 3 million rows ...
459 views

### xgboost: Is there a way to perform regression on rates/percentages data?

I have a dependent variable, $Y$, that is made up of rates/percentages data, so each value is between $0$ and $1$. I was attracted to the xgboost library because it allows focusing in on specific ...
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 ...
190 views

### Regression model for a count proces

In R I have data where head(data) gives ...
4k views

### Dummy coding a column in R with multiple levels

I have a dependent variable measuring the net revenue. One of the major predictor affecting this is "product" i.e. the product sold to the customer. My randomly sampled dataset contains 1.4 million ...
126 views

### Regression: How to deal with positive skewness in continuous target variable

I'm working on a regression problem. My aim is to "learn" the distribution of a continuous target $y$ as good as possible to make predictions. My model looks like: $$y_i=\beta X_i + u_i.$$ $y$ is ...
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 (...
2k views

### How to estimate the variance of regressors in scikit-learn?

Every classifier in scikit-learn has a method predict_proba(x) that predicts class probabilities for x. How to do the same thing ...
5k views

### Regression model to predict probability of rare event

I have a dataset with around 900.000 records, around 1000 of which are marked as positive (the studied event occurred). The probability of the event occurring is always low (i.e. < 0.1), and I ...
141 views

### Can we quantify how position within search results is related to click-through probability?

Suppose, for example, that the first search result on a page of Google search results is swapped with the second result. How much would this change the click-through probabilities of the two results? ...
115 views

### Confidence interval interpretation in linear regression when errors are not normally distributed

I've read that "If the error distribution is significantly non-normal, confidence intervals may be too wide or too narrow" (source). So, can anyone elaborate on this? When are the confidence intervals ...
716 views

### Choosing best methods for estimating the unknown parameters in a linear regression model

Given some dataset for prediction, for eg say I have different housing price prediction dataset: dataset 1 : 100 training and 100 testing sample, 50 feature dataset 2 : 100 training and 100 ...