Questions tagged [collinearity]

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Multicolinear Predictors Effect on Model

I know that multicolinear predictors in a model aren't ideal because it causes the model to be sensitive to very minor changes, which then reduces our ability to interpret the effects of each ...
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1 answer
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Does Multicollinearity affect Neural Networks?

Can someone explain to me like I'm five on why multicollinearity does not affect neural networks? I've done some research and neural networks are basically linear functions being stacked with ...
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Why does the regression model don't intelligently assign zero coefficient to one of the correlated variables?

One of the assumptions for Linear regression is no multicollinearity. Why does the regression model don't intelligently assign a zero coefficient to one of the correlated variables?
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Deriving VIF equation from the matrix form of Least Squares equation

I have been working through the derivation of the formula used to calculate the Variance Inflation Factor associated with a model. I am hoping to start with the Least Squares equation as defined in ...
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4 answers
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Multicollinearity vs Perfect multicollinearity for Linear regression

I have been trying to understand how multicollinearity within the independent variables would affect the Linear regression model. Wikipedia page suggests that only when there is a "perfect" ...
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2 answers
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Does PCA helps to include all the variables even if there is high collinearity among variables?

I have a dataset that has high collinearity among variables. When I created the linear regression model, I could not include more than five variables ( I eliminated the feature whenever VIF>5). But ...
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How does tree-based algorithms handle linearly combined features?

While I am aware that tree-based algorithms (e.g., DT, RF, XGBoost) are 'immune' to multi-collinearity, how do they handle linearly combined features? For example, is there is any additional value or ...
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3 answers
217 views

Whether Interaction terms should be included in Linear Regression analysis?

I am working on a linear model with 6 independent variables and when thinking about including an interaction I got lost. An interaction exists if the level of one independent variable is affected by ...
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1 answer
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Understanding one of the assumptions of linear regression: Multicollinearity

I've read that multicollinearity is one of the main assumptions of multivariate linear regression - Multicollinearity occurs when the independent variables are too highly correlated with each other. ...
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Possible harm in standardizing one-hot encoded features

While there may not be any added value in standardizing one-hot encoded features prior to applying linear models, is there is any harm in doing so (i.e., affecting model performance)? Standardizing ...
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1 answer
160 views

Collinearity between continuous and categorical variable

I have a medical dataset with features age, bmi, sex, gender, # of children, region, charges, smoker. Here smoker, gender, sex and region are categorical variables and others are numerical features. ...
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4 votes
2 answers
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what is the difference in terms namely Correlation, correlated and collinearity?

A website says Correlation refers to an increase/decrease in a dependent variable with an increase/decrease in an independent variable. Collinearity refers to two or more independent variables acting ...
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decision -tree regression to avoid multicollinearity for regression model?

I read in comments a recommendation for decision tree´s instead of linear models like neural network, when the dataset has many correlated features. Because to avoid multicollinearity. A similar ...
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Checking linearity for a linear regression model?

I've read that there are various assumptions associated with a multiple linear regression model which you should check/validate before getting too excited about your model results. One of these is the ...
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2 votes
1 answer
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Can GLM( generalized linear method) handle the collinearity between the predictor variables in a regression-analysis?

I'm a beginner in Machine learning and I've studied that collinearity among the predictor variables of a model is a huge problem since it can lead to unpredictable model behaviour and a large error. ...
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1 answer
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Handling near duplicate observations in a regression / Bayesian model

I am working on a model where the underlying data is inherently correlated by groups. So some of my observations are almost duplicates but not quite. The problem is pretty simple, I have a y ...
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9 votes
2 answers
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What is the meaning of a quadratic relation when r = 0?

A website (on page 4) says: The correlation coefficient is a measure of linear relationship and thus a value of r = 0 does not imply there is no relationship between the variables. For ...
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2 votes
2 answers
150 views

Transforming negative correlated non linear variable to linear positive correlated variable

At my office, I am stuck in a weird situation. I am asked to perform a regression algorithm on the data, in which the target variable is continuous having values range between 0.6 to 0.9 with 8 digits ...
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3 votes
1 answer
5k views

How to interpret Variance Inflation Factor (VIF) results?

From various books and blog posts, I understood that the Variance Inflation Factor (VIF) is used to calculate collinearity. They say that VIF till 10 is good. But I have a question. As we can see in ...
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1 answer
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Are time series algorithms immune towards collinearity?

I have a time series dataset with 63 features and a univariate dependent variable. This is my first major time series project, so I was wondering if algorithms like ARIMA and LSTM are immune towards ...
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6 votes
3 answers
1k views

Correlation vs Multicollinearity

I have been taught to check correlation matrix before going for any algorithm. I have a few questions around the same: Pearson Correlation is for numerical variables only. What if we have to check ...
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1 vote
3 answers
3k views

Multicollinearity(Variance Inflation Factor). Variables to remove before doing a model

I am doing an exercise of a Machine Learning System module in python that takes a dataset of cars (cylinders, year, consumption....) and asks for a model, being the variable to predict the consumption ...
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2 votes
2 answers
47 views

Multicolinearity & accurate weights of predictors

Let’s suppose that the stock value of various companies is the target of my models. I have some “internal” predictors e.g. yearly sales of each company, sum of salaries at each company etc. I have ...
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2 answers
79 views

How to measure variable contribution to an observation in a non-linear model?

Based on my model, if I decline someone due to their score, it should be able to provide some reasoning as to which variables mainly contributed to the decision to decline. Typically in Logistic ...
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2 votes
1 answer
982 views

Collinearity and Outlier Removal

I am playing with a credit fraud detection dataset at Kaggle. An imbalanced dataset with about 0.1% of fraud transaction. The features are 28 PCs out from a PCA exercise done by the data publisher + ...
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1 vote
1 answer
166 views

Detect multicollinearity in real-life, non-normally distributed data

I am currently trying to figure out whether my data (consisting of thousands of rows, some is numerical, and some are categorical, and some are ordinal) has multicollinearities or not. One thing I ...
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1 vote
1 answer
1k views

Should highly correlated features be omitted before applying Lasso?

I would greatly appreciate if you could let me know whether I should omit highly correlated features before using Lasso logistic regression (L1) to do feature ...
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