In other words, multicollinearity can exist when two independent variables are highly correlated. It can also happen if an independent variable is computed from other variables in the data set or if two independent variables provide similar and repetitive results.
It is a measure of multicollinearity in the set of multiple regression variables. The higher the value of VIF the higher correlation between this variable and the rest. If the VIF value is higher than 10, it is usually considered to have a high correlation with other independent variables.
However, when independent variables are correlated, it indicates that changes in one variable are associated with shifts in another variable. The stronger the correlation, the more difficult it is to change one variable without changing another.
When predictor variables are correlated, the precision of the estimated regression coefficients decreases as more predictor variables are added to the model.
Correlation is a term that refers to the strength of a relationship between two variables where a strong, or high, correlation means that two or more variables have a strong relationship with each other while a weak or low correlation means that the variables are hardly related.
In many datasets we find some of the features which are highly correlated that means which are some what linearly dependent with other features. These features contribute very less in predicting the output but increses the computational cost. This data science python source code does the following: 1.
In laymen's terms, two things have a correlation if the likelihood of one happening is strongly related to the likelihood of the other happening or not happening.
High: When the relationship among the exploratory variables is high or there is perfect correlation among them, then it said to be high multicollinearity.
Q.If two variables are highly correlated, what do you knowA.that they always go togetherB.that high values on one variable lead to high values on the other variableC.that there are no other variables responsible for the relationshipD.that changes in one variable are accompanied by predictable changes in the other1 more row
The easiest way is to delete or eliminate one of the perfectly correlated features. Another way is to use a dimension reduction algorithm such as Principle Component Analysis (PCA).
Multicollinearity occurs when two or more independent variables are highly correlated with one another in a regression model. This means that an independent variable can be predicted from another independent variable in a regression model.
Multicollinearity exists whenever an independent variable is highly correlated with one or more of the other independent variables in a multiple regression equation. Multicollinearity is a problem because it undermines the statistical significance of an independent variable.