General Machine Learning & Ethics

Question 1

What is the difference between supervised and unsupervised learning?

Answer 1

Supervised learning involves training a model on labeled data, while unsupervised learning deals with unlabeled data and seeks to find patterns or structures within it.

Question 2

What is Machine Learning and what does it do?

Answer 2

machine learning is the development of algorithms and statistical to teach computers to find patterns in data and make predictions or decisions without being explicitly programmed.

Question 3

Supervised ML

Answer 3

• uses labeled datasets
• to train algorithms to
• classify or predict outcomes.

Question 4

What is required for Supervised ML?

Answer 4

you need labeled data

Question 5

Unsupervised ML

Answer 5

uses algorithms to analyze and cluster unlabeled datasets.

Question 6

Once an algorithm is deployed, ____ learning will manage data as it comes in and classify or analyze it.

Answer 6

Unsupervised

Question 7

When is Linear Regression used?

Answer 7

Linear regression models are used when the result must be a continuous variable

Ex. predict rainfall amounts in inches

Question 8

Supervise ML - Classification

Answer 8

Classification models will deliver results as a categorical variable, where there is a finite set of values that the variable can be. two results: Will Rain or Won’t Rain.

Question 9

What is the 3 main goal of the analyze stage in machine learning?

Answer 9

1. Understanding response variables and how they’re structured. continuous? categorical?
2. Explore predictor variables.
3. Featuring Engineering

Question 10

Will my machine learning model change over time?

Answer 10

For a model to predict accurately, the data that it is making predictions on must have a similar distribution as the data on which the model was trained.

Because data distributions can be expected to drift over time, deploying a model is not a one-time exercise but rather a continuous process.

Continuous monitoring of incoming data can help retrain your model on newer data if the data distribution has deviated significantly from the original training data distribution.

Question 11

How can I determine that machine learning is the right solution?

Answer 11

1. Requires complex logic
2. Requires scalability
3. Requires personalization
4. Requires responsiveness

Question 12

What are the reasons to NOT use machine learning?

Answer 12

1. Can be solved with traditional algorithms
2. Does not require adapting to new data
3. Requires 100% accuracy
4. Requires full interpretability

Question 13

Is my data ready for a machine learning solution?

Answer 13

• Is it easily accessible?
• Does it respect privacy?
• Is it relevant?

Question 14

What is popularity bias in the context of machine learning?

Answer 14

Popularity bias refers to the phenomenon where more popular items are recommended more frequently by a system, often overlooking other items that could be just as pleasing to users.

Question 15

Why is it important for data professionals to prioritize fairness in their data

Answer 15

• reduce the potential for unintended consequences of machine learning applications, including the perpetuation of human biases.
• It is part of responsible data stewardship.

Question 16

What are some ethical considerations when building a model

Answer 16

1. ensuring informed consent for the use of personal data
2. considering who is affected by the model and potential harm
3. ensuring data is appropriate and representative
4. considering the explainability of the model’s predictions
5. and regularly reviewing and monitoring the model’s performance.

Question 17

What is a black box model?

Answer 17

A black box model refers to a type of model where it’s difficult to understand how the model arrived at its predictions.

Question 18

What is one way to evaluate model fairness?

Answer 18

One way to evaluate model fairness is by checking how the model’s error is distributed over a population. If the model mainly makes errors in specific, similar cases it could carry higher ethical risk.

Question 19

What is a potential risk in decision-making in machine learning?

Answer 19

A potential risk is exposing a business and the people it serves to negative consequences

Question 20

Where does bias in machine learning originate from?

Answer 20

from human bias

Question 21

Why can bias in machine learning be deceptive?

Answer 21

It can be deceptive because even though the bias stems from humans, the computer making the prediction can give the result an appearance of objectivity.

Question 22

What are some questions to help consider faireness of your model?

Answer 22

• If your model uses personal information, have these people given their consent for you to collect and use this data?
• Is there a way for them to withdraw their consent?
• Are they aware of what you’re doing with their information?

Question 23

Explain the bias-variance trade-off in machine learning.

Answer 23

the trade-off between a model’s ability to fit the training data (low bias) and its ability to generalize to new, unseen data (low variance).

• High bias can result in underfitting,
• High variance can lead to overfitting

Balancing these aspects is essential for optimal model performance.