Questions tagged [collinearity]
The collinearity tag has no usage guidance.
21
questions
0
votes
1answer
24 views
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 ...
0
votes
3answers
102 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 ...
1
vote
1answer
91 views
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.
...
6
votes
2answers
478 views
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 ...
0
votes
1answer
46 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. ...
4
votes
2answers
124 views
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 ...
1
vote
1answer
508 views
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 ...
2
votes
2answers
133 views
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 ...
2
votes
1answer
364 views
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. ...
2
votes
1answer
32 views
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 ...
8
votes
2answers
1k views
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
...
2
votes
1answer
96 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 ...
3
votes
1answer
3k 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 ...
0
votes
1answer
104 views
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 ...
3
votes
3answers
786 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 ...
1
vote
3answers
2k 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 ...
2
votes
2answers
43 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 ...
3
votes
2answers
62 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 ...
2
votes
1answer
939 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 + ...
1
vote
0answers
128 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 ...
1
vote
1answer
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 ...
