Table of Contents
- 1 What to do if features are highly correlated?
- 2 Is high correlation good or bad?
- 3 How do you remove a correlation?
- 4 What does a high correlation mean?
- 5 What is the difference between feature selection and dimensionality reduction?
- 6 What are the different types of feature selection methods?
- 7 How do you select the selected features in a regression?
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).
Is high correlation good or bad?
Strength: The greater the absolute value of the correlation coefficient, the stronger the relationship. The extreme values of -1 and 1 indicate a perfectly linear relationship where a change in one variable is accompanied by a perfectly consistent change in the other.
Why is high correlation bad?
The stronger the correlation, the more difficult it is to change one variable without changing another. It becomes difficult for the model to estimate the relationship between each independent variable and the dependent variable independently because the independent variables tend to change in unison.
How do you remove a correlation?
You can’t “remove” a correlation. That’s like saying your data analytic plan will remove the relationship between sunrise and the lightening of the sky.
What does a high correlation mean?
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.
How do you remove a correlation from a variable?
In some cases it is possible to consider two variable as one. If they are correlated, they are correlated. That is a simple fact. You can’t “remove” a correlation.
What is the difference between feature selection and dimensionality reduction?
Feature selection is also related to dimensionally reduction techniques in that both methods seek fewer input variables to a predictive model. The difference is that feature selection select features to keep or remove from the dataset, whereas dimensionality reduction create a projection of the data resulting in entirely new input features.
What are the different types of feature selection methods?
There are a lot of ways in which we can think of feature selection, but most feature selection methods can be divided into three major buckets Filter based: We specify some metric and based on that filter features. An example of such a metric could be correlation/chi-square.
How do you select the most relevant features from the data?
Filter-based feature selection methods use statistical measures to score the correlation or dependence between input variables that can be filtered to choose the most relevant features. Statistical measures for feature selection must be carefully chosen based on the data type of the input variable and the output or response variable.
How do you select the selected features in a regression?
Feature selection is performed using Pearson’s Correlation Coefficient via the f_regression () function. Running the example first creates the regression dataset, then defines the feature selection and applies the feature selection procedure to the dataset, returning a subset of the selected input features.