Is highly correlated factors in a prediction model a problem?

tl;dr - highly correlated variables are more a problem for interpretation than prediction. If you only care about the latter, you don't care ...

Having highly correlated factors in a prediction model is not an issue (especially after bagging). But in a model where you are trying to explain the target, ...

In supervised learning, why is it bad to have correlated features?

Correlated features in general don't improve models (although it depends on the specifics of the problem like the number of variables and ...

12.3 - Highly Correlated Predictors | STAT 501

High multicollinearity among predictor variables does not prevent good, precise predictions of the response within the scope of the model. Well, okay, it's not ...

What we should do with highly correlated features? - Stack Overflow

Depending on the features and the model, correlated features might not always harm the performance of the model but that is a real risk. You can ...

5.11 Dealing with correlated predictors

Highly correlated predictors can lead to collinearity issues and this can greatly increase the model variance, especially in the context of regression.

In practice, why is it bad to include highly correlated x variables in a ...

Since you are predicting an outcome you want your factors to be independent. Correlation indicates two or more factors are providing your model ...

10.6 - Highly Correlated Predictors | STAT 462

High multicollinearity among predictor variables does not prevent good, precise predictions of the response within the scope of the model. Well, okay, it's not ...

In Supervised Learning, Why Is It Bad to Have Correlated Features?

In supervised learning, correlated features introduce multicollinearity, where predictor variables are highly correlated, potentially causing ...

Multicollinearity in Regression Analysis: Problems, Detection, and ...

Multicollinearity occurs when independent variables in a regression model are correlated. This correlation is a problem because independent variables should be ...

If you are interested just in predictions, usually no problems. If you want make inference on parameter values with some causal interpretation, ...

How much should I care about a multicollinearity problem? - Statalist

I am working with an OLS model to make predictions about consumption. This model has three explanatory variables, its results seem to be sound, ...

Why is it so important to avoid data with high correlation? How can ...

Using highly correlated predictors in techniques like linear regression can result in highly unstable models, numerical errors, and very poor prediction ...

Why we have to remove highly correlated features in Machine ...

Impact: Interpretable models are essential for understanding the factors influencing predictions. Removing correlated features aids in ...

Regression with Highly Correlated Predictors: Variable Omission Is ...

However, researchers often encounter situations in which some independent variables exhibit high bivariate correlation, or may even be collinear ...

Correlation in machine learning — All you need to know - Medium

Multicollinearity occurs when two or more predictor variables in a model are highly linearly correlated with each other. ... problem. No ...

Prediction vs. Causation in Regression Analysis | Statistical Horizons

In causal inference, multicollinearity is often a major concern. The problem is that when two or more variables are highly correlated, it can be ...

5.9 Correlation, causation and forecasting - OTexts

A closely related issue is multicollinearity, which occurs when similar information is provided by two or more of the predictor variables in a multiple ...

What is Multicollinearity? | Causes, Effects and Detection Using VIF

Multicollinearity is a statistical phenomenon that occurs when two or more independent variables in a regression model are highly correlated, ...

Multicollinearity: Meaning, Examples, and FAQs - Investopedia

High multicollinearity demonstrates a correlation between multiple independent variables, but it is not as tight as in perfect multicollinearity. Not all data ...