You work as a machine learning specialist at a mining and minerals company.
Your company has asked you to build a model that predicts the efficacy of a given drilling site.
Your model training dataset has a large number of features.
For your modeling exploration, you have chosen to use regression models, such as linear regression and logistic regression.
During exploratory data analysis, you notice a high correlation between many features that you believe will make your model unstable.
How can you address the problem of having too many highly correlated features?
Click on the arrows to vote for the correct answer
A. B. C. D.When working with a dataset that has many highly correlated features, a common challenge is to choose which features to include in the model. If too many highly correlated features are included, the model can become unstable, and predictions may not be accurate.
There are several ways to address this problem, including:
A. Use a Cramer's V correlation coefficient: Cramer's V is a measure of association between two nominal variables, and it can be used to identify highly correlated features in a dataset. However, Cramer's V is only appropriate for nominal data, so it may not be useful for datasets with continuous or ordinal data.
B. Use Principal Component Analysis (PCA) to reduce the dimensionality of the dataset: PCA is a technique that can be used to reduce the dimensionality of a dataset by transforming the original features into a smaller set of principal components. These principal components are orthogonal and uncorrelated, which can help to address the problem of highly correlated features.
C. Modify highly correlated features using vector multiplication: This is not a common technique for addressing the problem of highly correlated features. While it is possible to modify highly correlated features using vector multiplication, it may not be a practical or effective solution.
D. Modify highly correlated features using a Spearman correlation coefficient: The Spearman correlation coefficient is a measure of association between two variables, and it can be used to identify highly correlated features in a dataset. Unlike Cramer's V, the Spearman correlation coefficient is appropriate for continuous and ordinal data. However, like Cramer's V, it does not offer a solution to the problem of highly correlated features.
In this case, the most appropriate solution would be to use PCA to reduce the dimensionality of the dataset. This would help to address the problem of highly correlated features and improve the stability and accuracy of the model.