You work for a car manufacturer as a machine learning specialist.
Your marketing team wants to use a marketing strategy to market to different consumer segments based on how the features of each of their cars resonate with their customer base. The dataset with which you have to work contains many features about each car, such as color, size, number of doors, number of speakers, type of roof, type of auto-assist, etc.
Through your exploratory modeling, you have found many of these features are redundant, meaning they don't offer anything further to your algorithm's performance. Your dataset contains a large number of observations and a large number of features.
How would you solve this redundant feature problem most efficiently and expeditiously?
Click on the arrows to vote for the correct answer
A. B. C. D.Answer: C.
Option A is incorrect.
The XGBoost algorithm is used to predict a target variable in a very fast and efficient manner.
However, the XGBoost will not automatically adjust for redundant features.
The redundant features will act as a performance drag since you have a large number of features and a large number of observations.
Option B is incorrect.
Removing the redundant features outright creates the risk of information loss.
A better solution is to find composites of uncorrelated features, which is the technique used by Principal Component Analysis.
Option C is correct.
Principal Component Analysis is a machine learning algorithm that reduces dimensionality within your data without sacrificing information.
It does this by finding composites of features that are uncorrelated.
Option D is incorrect.
The Random Cut Forest algorithm is used to find atypical data points in a dataset.
Therefore it will not help find redundant features.
The redundant features will act as a performance drag since you have a large number of features and a large number of observations.
Reference:
Please see the Amazon SageMaker developer guide titled Using Amazon SageMaker Built-in Algorithms, the Amazon SageMaker developer guide titled Principal Component Analysis (PCA) Algorithm, and the article titled Automatically Redundant Features Removal for Unsupervised Feature Selection via Sparse Feature Graph.
The most efficient and expeditious way to solve the redundant feature problem in a dataset with a large number of observations and features is to use Principal Component Analysis (PCA) to reduce the number of features. Therefore, the correct answer is C.
PCA is a technique used to reduce the dimensionality of large datasets while retaining as much of the original variability as possible. In other words, it transforms a large number of variables into a smaller set of new variables, called principal components, which explain most of the variance in the original data.
The steps to implement PCA in this scenario would be as follows:
Standardize the data: PCA is sensitive to the scale of the variables, so it's necessary to standardize the data before applying PCA to ensure that all variables have the same weight.
Calculate the covariance matrix: This matrix represents the relationships between all pairs of variables in the dataset. It's used to find the direction of the principal components.
Compute the eigenvectors and eigenvalues of the covariance matrix: Eigenvectors represent the direction of the principal components, while eigenvalues represent the amount of variance explained by each component.
Select the number of principal components: A common approach is to select the number of components that explain a significant amount of variance in the data. For example, one could choose to retain components that explain 80% of the total variance.
Project the data onto the new feature space: Once the principal components are identified, the original data can be projected onto the new feature space, resulting in a reduced set of features that captures most of the variability in the data.
In summary, using PCA to reduce the number of features is an efficient and expeditious way to solve the redundant feature problem in a large dataset with a large number of observations and features.