MLT Unit 2

Principal Component Analysis is one of the algorithms in ...

• Unsupervised Learning

• Supervised Learning

• Regression

• Classification

What does an Explained Variance Ratio value of 0.34 mean in Principal Component Analysis?

• The variance of the data is 34%

• The information about the overall data when reduced is 34%

• The amount of data that is reduced is 34%

• The prediction error on the data is 34%

The learning process that has unlabeled data is ....

• Unsupervised

• Supervised

• Clustering

• Dimensionality Reduction

In performing dimensionality reduction, what is done to reduce the dimension?

• Transforming the columns/features so that the dimension becomes smaller

• Dropping columns/features so that the dimension becomes smaller

• Dropping rows/data so that the dimension becomes smaller

• Transforming the rows/data so that the dimension becomes smaller

What is the primary goal of dimensionality reduction techniques like PCA?

• To improve the accuracy of predictions

• To increase the number of features

• To eliminate all noise from the data

• To simplify the model while retaining important information

Which of the following statements is true about the covariance matrix in PCA?

• It measures the correlation between different features

• It is always a diagonal matrix

• It is not relevant in the PCA process

• It is used to increase the dimensionality of the data