Question

1. Match the following Machine Learning algorithms to their descriptions: 1. Linear Regression                          &nbsp

1. Match the following Machine Learning algorithms to their descriptions:

1. Linear Regression                                      A. A system of assigning values by sorting data through a series of questions which can be represented in a flow     chart.

2.  Linear Discriminant Analysis B. A classifier which uses data points of known class to vote on which class a new point would belong in.

3. Decision Tree                                                   C. An algorithm that calculates a line to represent a set of data which is more or less correlated to it on a scale of -1 to 1.

4. Naive Bayes                                                            D. A linear classification algorithm for more than 2 classes

5. K-Nearest Neighbors                                  E. A predictive model that calculates probability using Bayes Theorem.

2. In 2-3 sentences each, explain 2 reasons that a machine learning classifier could be bad despite a correctly-classified percentage above 70%.

3. If I roll two dice, what are the odds that both will be the same number? Note: You must show your work for full credit.

1. Linear Regression - C. An algorithm that calculates a line to represent a set of data which is more or less correlated to it on a scale of -1 to 1.

2. Linear Discriminant Analysis - D. A linear classification algorithm for more than 2 classes

3. Decision Tree - A. A system of assigning values by sorting data through a series of questions which can be represented in a flow chart.

4. Naive Bayes - E. A predictive model that calculates probability using Bayes Theorem.

5. K-Nearest Neighbors - B. A classifier which uses data points of known class to vote on which class a new point would belong in.