Data Analytics Overview

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Questions and Answers

What is the primary purpose of prescriptive actionability in XAI?

To explain the inner workings of AI models

To enhance the performance of technical staff

To reduce the need for documentation

To recommend human decisions based on AI results (correct)

Which of the following best describes the role of managers in the context of XAI?

They prioritize actionable insights to achieve real objectives (correct)

They primarily oversee regulatory compliance

They are mainly concerned with the technical details of AI

They focus solely on understanding AI functionalities

What do accumulated local effects (ALE) plots primarily indicate?

The distribution of X and Y values through visual representation (correct)

The predicted outcomes based on unrelated factors

The maximum potential of AI in a given situation

The correlation between all variables involved

Which audience benefits the most from understanding actionable goals in XAI?

Users who rely on AI outputs for decisions Signup and view all the answers

What is a key feature of interactive explanations in XAI?

They enable collaboration between users and AI models Signup and view all the answers

What percentage of the sample of senior citizens has no insurance?

15% Signup and view all the answers

Which analytics approach focuses on predicting the future based on past data?

Predictive analytics Signup and view all the answers

What is the primary focus of prescriptive analytics?

Identifying input features that affect outcomes Signup and view all the answers

What is a legitimate use of predictive analytics?

Anticipating uncontrollable outcomes Signup and view all the answers

What does a lower Mean Absolute Error (MAE) indicate in predictive modeling?

Higher accuracy in predictions Signup and view all the answers

Which type of analytics is primarily concerned with past performance to improve future outcomes?

Prescriptive analytics Signup and view all the answers

What is the estimated percentage of individuals covered by Medicaid in the surveyed senior population?

9% Signup and view all the answers

Automated machine learning (AutoML) is particularly useful for which aspect of predictive analytics?

Providing excellent predictive modeling results Signup and view all the answers

What level of control allows managers to have total influence over the values of a concept?

High control Signup and view all the answers

What should managers do when they have low or no control over a concept?

Measure and observe its values Signup and view all the answers

Which stage of data analytics helps managers understand past data to see the big picture?

Descriptive analytics Signup and view all the answers

What type of analytics suggests how managers may shape the future in their favor?

Prescriptive analytics Signup and view all the answers

Which situation best describes low control over a concept?

Managers influence outcomes, but external factors play a significant role Signup and view all the answers

Which of the following accurately describes data visualization?

It provides an intuitive understanding of data that is accurate. Signup and view all the answers

In the context of healthcare insurance management, which variable is considered the prediction target?

number of hospital stays Signup and view all the answers

Why is it important for managers to anticipate the effects of controllable concepts?

It helps in planning proactive responses Signup and view all the answers

What is one of the goals for managers at the health insurance provider regarding their members?

To minimize healthcare costs while ensuring adequate care Signup and view all the answers

What can result from high control over a concept?

Ability to shape the concept towards desired outcomes Signup and view all the answers

How does prescriptive analytics differ from other types of analytics?

It recommends actions based on predictions and analysis of past data Signup and view all the answers

What is a managerial implication of having no control over a concept?

Managers must react to changes rather than control them Signup and view all the answers

Which demographic variable is tied to self-perceived health status in the dataset?

Age in years Signup and view all the answers

Why is data visualization important in the context of analytics?

It simplifies the communication of complex data insights. Signup and view all the answers

What is the primary benefit of algorithmic transparency in XAI?

It simplifies model operations to prevent misconceptions. Signup and view all the answers

What is one characteristic of the descriptive analytics stage?

It emphasizes analyzing and presenting past data. Signup and view all the answers

Who primarily benefits from realistic representation in XAI?

Managers, focusing on the AI's real-world correspondence. Signup and view all the answers

What does ethical responsibility in XAI aim to highlight regarding AI models?

The significance of fairness and the identification of biases. Signup and view all the answers

Which of the following describes the role of domain experts in realistic representation?

They verify the model’s real-world correspondence. Signup and view all the answers

What dilemma is associated with ethical responsibility in AI?

The balance between accuracy and explainability. Signup and view all the answers

Which of the following is NOT a primary goal of stakeholders in XAI?

User Engagement Signup and view all the answers

What characteristic does informative algorithmic transparency provide?

Simplified explanations to prevent misconceptions. Signup and view all the answers

What is the main focus of ethical responsibility in relation to AI models?

Protecting user personal information and privacy. Signup and view all the answers

What is the primary purpose of identifying the Ultimate concept in a project?

To have a distinct target or label for supervised learning Signup and view all the answers

Which of the following represents a key managerial implication of concepts categorized as Relevant?

Take action to shape these concepts if controllable Signup and view all the answers

What should managers do with concepts that are categorized as Not Relevant?

Limit resources spent on observation and control Signup and view all the answers

How many Ultimate concepts should typically be present in a project?

Only one distinct Ultimate concept Signup and view all the answers

Which of the following statements correctly describes when a concept may shift from Not Relevant to Relevant?

Whenever the model is reassessed Signup and view all the answers

Which of the following best describes a concept's Ultimate relevance in supervised learning?

It can be highly desirable or undesirable Signup and view all the answers

What action should managers take regarding concepts that are not under their control?

Observe, anticipate and react to changes Signup and view all the answers

Which step comes immediately after gathering all available concepts in the actionable explanation process?

Classify concepts according to their ultimate importance Signup and view all the answers

Study Notes

Data Analytics Stages

Three stages of data analysis exist: descriptive, predictive, and prescriptive analytics.
Descriptive analytics involves analyzing past data to identify patterns and trends.
Predictive analytics uses past data to predict future outcomes.
Prescriptive analytics provides recommendations based on predicted outcomes, aiming for better future results.

Descriptive Analytics and Data Visualization

Descriptive analytics analyzes past data to understand patterns and trends.
Data visualization effectively communicates insights from data analysis, creating engaging and insightful stories.
Intuitive data visualization accurately translates data into understandable interpretations, avoiding misleading information.
Important to understand the limitations of data visualization by avoiding misinterpretations and understanding how to avoid the tricks outlined in the book "How to Lie with Statistics".

Role-Playing Exercise (Health Insurance)

Managers in a health insurance provider role contribute to understanding the healthcare costs of their members.
Health insurance in the United States generally covers all medical costs.
The role-playing exercise focused on health insurance managers for senior citizens (66 years and older).
The goal was to provide adequate healthcare while managing costs and member health.

US National Medical Expenditure Survey (NMES) Dataset

The NMES dataset involves 4,406 senior citizens from the general population.
The sample includes individuals aged 66 and older; not specific health insurance members.
The dataset includes variables like hospital stays, health status, chronic illnesses, and demographics.

Al-Powered Descriptive Analytics with Microsoft Excel

Excel Al simplifies data analysis using AI tools.

Predictive Analytics

Predictive analytics analyzes past data to predict future events, based on the assumption that the future will mirror the past.
It focuses on highly accurate estimations and predictions.
Predictive analytics is useful in prioritizing tasks, anticipating outcomes with little control, and benchmarking performance to identify improvements.

Best-Performing Model for Predicting Hospital Stays

The Mean Absolute Error (MAE) is a metric for assessing the accuracy of a model.
Lower MAE values indicate more accurate predictions.
The analysis presented graphs showing model performance in predicting hospital stays.

Gradient Boosted Tree Model

Gradient Boosted Trees model helps understand factors impacting hospital stay counts.
Important factors for predicting hospital stays were identified through this model.
Variables ranked by their contribution to the model's prediction accuracy were shown.

Prescriptive Analytics

Prescriptive analytics analyses past data to provide guidance on interventions for improving future outcomes.
The focus is on input factors (factors that affect the outcome), aiming to improve the outcome, not replicate the past.
Using interpretability, prioritizing, and anticipating outcomes, and benchmarking gives more valuable insights.

Accurate Predictions

An accurate prediction is valuable; a goal for many models.
State-of-the-art techniques achieve prediction accuracy.
Simulations help estimate the effects of different values.

Explainable Al (XAI)

XAI is a form of Artificial Intelligence providing explanations about model predictions, outcomes, and actions.
Stakeholders include managers, users, developers, and regulators.
Goals of XAI include algorithmic transparency, realistic representation, ethical responsibility, and prescriptive actionability.

XAI for Algorithmic Transparency

XAI for algorithmic transparency explains how the AI model reached its conclusions using easily understandable language.
This is achieved by simplifying internal operations while avoiding model misconceptions.
This usually benefits developers most.

XAI for Realistic Representation

XAI for realistic representation explains the correspondence between the AI model predictions and reality.
This understanding often requires domain input from experts.
Different types of models may correspond to reality in different ways.

XAI for Ethical Responsibility

XAI for ethical responsibility provides information with values like fairness and privacy in mind.
Bias elimination in training data is part of fairness.
Consideration for user privacy and data protection is important.

XAI for Prescriptive Actionability

XAI for prescriptive actionability supports action recommendations from the AI.
This usually involves finding causal relationships and collaborative improvements.
This is useful for persuading managers and also for helping users better understand the goals of the model.

Relationships Among XAI Stakeholders' Goals

A diagram outlining the relationships among the stakeholders' goals shows how they relate to each other, suggesting that ethical responsibility (ER) needs explaining (XAI); however, algorithmic transparency is just part of the needed explanation by XAI for realistic representations.

Accumulated Local Effects (ALE) Plots for XAI

ALE plots visualize the impact of different X (variables) values on the Y value (a prediction variable) for XAI.
This helps in understanding which factors affect outcomes and to what degree.

Actionable Explanations

Actionable explanations in XAI provide insights for managerial action based on predictions.
The analysis should highlight relevant factors, their influence, and the potential for managerially actionable change.

XAI Process

The process of actionable explanations involves gathering relevant concepts.
Actionable explanations should be classified.
Analysis of various methodologies is vital for achieving useful and meaningful conclusions.

Relevance of Concepts in XAI

Useful and relevant concepts are crucial for effective XAI, categorized appropriately.
Relevant concepts must affect the outcome being predicted, classified in ways that help to predict future outcomes.
Non-relevant concepts, those which do not affect the predicted outcome can be ignored and resources can be saved by not being investigated.

Controllability of Concepts in XAI

Controllability of concepts means how much managers can influence the actions, values, and outcomes.
Understanding the different levels of control (high, low, no) is important in managing action.

Evaluation of the Teacher

Provide comments instead of just numerical scores.
Comment on aspects you liked or areas you think need improvement.
Focus comments on the in-class session, not on homework or other instructors.

Conclusion

Summary of the three stages of data analysis:
Descriptive (past data and patterns), predictive (future prediction) and prescriptive (actionable recommendations)
Importance of XAI to understand machine learning model results.

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Description

Explore the three stages of data analytics: descriptive, predictive, and prescriptive. Learn how these stages work together to provide insights and recommendations for better decision-making. The quiz also covers the effective use of data visualization and its limitations.