Data Science and Machine Learning (Theory and Projects) A to Z - Feature Extraction: Dimensionality Reduction Pipelines

The video tutorial covers various feature extraction and dimensionality reduction techniques, focusing on methods like PCA, kernel PCA, ISOMAP, and LLE. It explains how to implement these techniques using scikit-learn, including data preparation and importing necessary packages. The tutorial also highlights the importance of building pipelines for efficient data processing and discusses the challenges of using neighborhood-based methods due to their computational intensity.

Which of the following is NOT a dimensionality reduction technique discussed in the video?

• ISOMAP

• Linear Regression

• PCA

• Kernel PCA

What is the primary purpose of using PCA in data analysis?

• To perform supervised learning

• To reduce the dimensionality of data

• To increase the number of features

• To create new data points

What is the role of the 'fit' function in scikit-learn?

• To split the data into train and test sets

• To train the model on the data

• To visualize the data

• To transform the data

What is a common challenge when using neighborhood-based techniques like ISOMAP?

• They do not work with large datasets

• They require labeled data

• They are computationally intensive

• They are too fast

Why might you choose to use a kernel in PCA?

• To handle non-linear data

• To perform feature scaling

• To reduce the number of samples

• To increase computation speed

What is the benefit of using pipelines in scikit-learn?

• They increase the dimensionality of data

• They simplify the process of applying multiple transformations

• They are only used for visualization

• They allow for parallel processing

What is the purpose of the 'transform' function in scikit-learn?

• To visualize the data

• To fit the model to the data

• To apply the learned transformation to the data

• To split the data into train and test sets

Which method is used to select the best features in a pipeline?

• SelectKBest

• ISOMAP

• PCA

• Kernel PCA

What is a potential downside of using RBF kernel in kernel PCA?

• It requires labeled data

• It reduces the number of features

• It may not be suitable for all datasets

• It is always the best choice

What is a key advantage of using dimensionality reduction techniques?

• They require more computational resources

• They reduce the risk of overfitting

• They increase the complexity of the model

• They are only useful for visualization