Fundamentals of Machine Learning and Deep Learning Quiz

1. Supervised vs. Unsupervised Learning

   Which of the following best describes supervised learning, using the example of classifying emails as 'spam' or 'not spam'?

   1. It clusters emails based on their text length only.
   2. It uses labeled data to train the model to classify emails.
   3. It generates new emails similar to the training data.
   4. It identifies patterns in emails without using any labels.
   5. It processes only numeric data and ignores text.

2. Role of Linear Regression

   What is the main purpose of linear regression in machine learning when analyzing a set of points on a graph?

   1. To perform data duplicition before analysis.
   2. To increase the number of features automatically.
   3. To find the best fitting straight line that predicts the relationship between variables.
   4. To group the points into different clusters based on distance.
   5. To convert categorical variables into numbers.

3. Linear Regression Equation

   For a linear regression problem with one input feature x and one output y, which equation correctly describes the model?

   1. y = wx + b
   2. y = wxy + b
   3. y = x^2 + c
   4. y = wx - y
   5. y = wx + x

4. Performance Metric for Regression

   When evaluating a linear regression model, which metric is commonly used to measure its performance?

   1. Recall Ratio
   2. Confussion Metric
   3. K-means Clustering
   4. Mean Squared Error
   5. F1 Scoreing

5. Purpose of Principal Component Analysis

   What is the main purpose of Principal Component Analysis (PCA) when working with a dataset containing many features?

   1. To reduce the dimensionality of the data while preserving important information.
   2. To perform text translation automatically.
   3. To label each data point with a predicted class.
   4. To generate new output variables unrelated to the input.
   5. To always increase the number of variables in the dataset.