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Free AIP-210 Exam Braindumps (page: 7)

Page 6 of 23
PDF with 90 Questions

R-squared is a statistical measure that:

  1. Combines precision and recall of a classifier into a single metric by taking their harmonic mean.
  2. Expresses the extent to which two variables are linearly related.
  3. Is the proportion of the variance for a dependent variable thaf' s explained by independent variables.
  4. Represents the extent to which two random variables vary together.

Answer(s): C

Explanation:

R-squared is a statistical measure that indicates how well a regression model fits the data. R-squared is calculated by dividing the explained variance by the total variance. The explained variance is the amount of variation in the dependent variable that can be attributed to the independent variables. The total variance is the amount of variation in the dependent variable that can be observed in the data. R-squared ranges from 0 to 1, where 0 means no fit and 1 means perfect fit.



Which of the following equations best represent an LI norm?

  1. |x| + |y|
  2. |x|+|y|^2
  3. |x|-|y|
  4. |x|^2+|y|^2

Answer(s): A

Explanation:

An L1 norm is a measure of distance or magnitude that is defined as the sum of the absolute values of the components of a vector. For example, if x and y are two components of a vector, then the L1 norm of that vector is |x| + |y|. The L1 norm is also known as the Manhattan distance or the taxicab distance, as it represents the shortest path between two points in a grid-like city.



Which of the following statements are true regarding highly interpretable models? (Select two.)

  1. They are usually binary classifiers.
  2. They are usually easier to explain to business stakeholders.
  3. They are usually referred to as "black box" models.
  4. They are usually very good at solving non-linear problems.
  5. They usually compromise on model accuracy for the sake of interpretability.

Answer(s): B,E

Explanation:

Highly interpretable models are models that can provide clear and intuitive explanations for their predictions, such as decision trees, linear regression, or logistic regression. Some of the statements that are true regarding highly interpretable models are:
They are usually easier to explain to business stakeholders: Highly interpretable models can help communicate the logic and reasoning behind their predictions, which can increase trust and confidence among business stakeholders. For example, a decision tree can show how each feature contributes to a decision outcome, or a linear regression can show how each coefficient affects the dependent variable.
They usually compromise on model accuracy for the sake of interpretability: Highly interpretable models may not be able to capture complex or non-linear patterns in the data, which can reduce their accuracy and generalization. For example, a decision tree may overfit or underfit the data if it is too deep or too shallow, or a linear regression may not be able to model curved relationships between variables.



Which two of the following decrease technical debt in ML systems? (Select two.)

  1. Boundary erosion
  2. Design anti-patterns
  3. Documentation readability
  4. Model complexity
  5. Refactoring

Answer(s): C,E

Explanation:

Technical debt is a metaphor that describes the implied cost of additional work or rework caused by choosing an easy or quick solution over a better but more complex solution. Technical debt can accumulate in ML systems due to various factors, such as changing requirements, outdated code, poor documentation, or lack of testing. Some of the ways to decrease technical debt in ML systems are:
Documentation readability: Documentation readability refers to how easy it is to understand and use the documentation of an ML system. Documentation readability can help reduce technical debt by providing clear and consistent information about the system's design, functionality, performance, and maintenance. Documentation readability can also facilitate communication and collaboration among different stakeholders, such as developers, testers, users, and managers. Refactoring: Refactoring is the process of improving the structure and quality of code without changing its functionality. Refactoring can help reduce technical debt by eliminating code smells, such as duplication, complexity, or inconsistency. Refactoring can also enhance the readability, maintainability, and extensibility of code.






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