You are a machine learning specialist at an online car retailer.
Your machine learning team has been tasked with building models to predict car sales and customer conversion rates.
The dataset you are using has a large number of features, over 1,000
Your team plans to use linear models, such as linear regression and logistic regression, in a SageMaker Studio environment.
When your team performs exploratory data analysis in their SageMaker Studio jupyter notebooks, they notice that many features are highly correlated with each other.
Your tech lead has indicated that this may make your models unstable. Which option would help you reduce the impact of having such a large number of features?
Click on the arrows to vote for the correct answer
A. B. C. D.Answer: B.
Option A is incorrect.
Dot product or matrix multiplication will not reduce the impact of having 1,000 features in your dataset.
It is used in deep learning for operations such as the Softmax function.
Option B is CORRECT.
Principal Component Analysis (PCA) is a very common technique used in machine learning to reduce the dimensionality of your dataset.
Reducing the dimensionality reduces the impact of having a large number of correlated features.
Option C is incorrect.
One-hot-encoding is a technique used to encode categorical data.
One-hot-encoding will actually increase the number of features in your dataset.
Option D is incorrect.
TF-IDF, or Term Frequency Inverse Document Frequency, is used to indicate the importance of a word in a document in a collection or corpus.
You are dealing with sales data and conversion rates, not text datasets.
Reference:
Please see the Amazon SageMaker developer guide titled Amazon SageMaker Studio.
Please refer to the Data Science Bootcamp article titled Understand Dot Products Matrix Multiplications Usage in Deep Learning in Minutes - beginner friendly tutorial.
Please see the Data Science Bootcamp article titled Understand the Softmax Function in Minutes.
Please see the article titled A simple guide to One-hot Encoding, tf and tf-idf Representation.
When working with a dataset with a large number of features, it is important to carefully consider the impact that these features can have on the performance of the machine learning model. Highly correlated features can negatively impact the performance of linear models such as linear regression and logistic regression, as they can cause instability and overfitting.
Option A, using dot product on the highly correlated features, is not a recommended approach to reduce the impact of having a large number of features. While this technique can be useful in certain situations, it does not address the issue of highly correlated features and may actually exacerbate the problem.
Option C, one-hot-encoding the highly correlated features, is also not a recommended approach. One-hot encoding creates new binary features for each unique value in a categorical variable, which can increase the dimensionality of the dataset and potentially lead to overfitting.
Option D, using TF-IDF encoding, is not applicable to this problem as it is a technique commonly used in natural language processing to represent text as numerical features.
Option B, using Principal Component Analysis (PCA) to create a new feature space, is a suitable approach to reduce the impact of having a large number of highly correlated features. PCA is a technique that can be used to transform a set of correlated features into a smaller set of uncorrelated features, known as principal components. These principal components capture the most important information from the original features and can be used as input to the machine learning model.
By reducing the number of features and removing correlations, PCA can improve the stability and performance of linear models such as linear regression and logistic regression. Additionally, by reducing the dimensionality of the dataset, PCA can also reduce the computational resources required for training the model.
Therefore, the correct option to reduce the impact of having a large number of highly correlated features is B. Use Principal Component Analysis (PCA) to create a new feature space.