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Questions tagged [collinearity]

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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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0answers
60 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 ...
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1answer
15 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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2answers
109 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 ...
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1answer
96 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
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2answers
82 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
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1answer
147 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. ...
1
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1answer
15 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 ...
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0answers
12 views

Does Generalised Additive Models handle Multicollinearity out of the box while building a Regression Model in Python

While Building a Generalised Additive Model for Logistic Regression, will the multicollinearity between the predictor variables are taken care of out of the box by the algorithm or do we need to ...
8
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2answers
601 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 ...
1
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1answer
36 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 ...
2
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1answer
750 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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1answer
32 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 ...
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3answers
473 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
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2answers
863 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
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2answers
36 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
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2answers
56 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
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1answer
836 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
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0answers
113 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
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1answer
963 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 ...