Advanced Analytics and Machine Learning

What type of variable is typically used in classification algorithms?

Which of the following is NOT a performance metric used in classification?

What is the primary purpose of dimensionality reduction algorithms?

Clustering algorithms are primarily used to:

What type of algorithm would most likely be used for understanding relationships within a network?

Which performance metric indicates the proportion of actual positives correctly identified?

In the context of classification, what does F1 score measure?

Which type of algorithm would best suit a scenario where the number of clusters needs to be determined from data?

Which of the following techniques is NOT considered a part of advanced analytics?

Which process is related to the discovery and development of analytics?

What role does programming language play in the analytics process?

Which of the following best describes the relationship between IT and business in analytics?

Advanced Analytics Techniques

Advanced analytics uses various techniques to solve problems
Techniques include machine learning, statistical analysis, forecasting, text analytics, and optimization

Operationalizing Analytics

Operationalizing analytics involves a process from data to insight, to decision, culminating in ROI
Steps include programming language flexibility, model governance, monitoring and improvement, automation, operational decision flow, quicker data discovery, any data, intelligent data preparation, discovery, and development of analytics, data preparation, and analytics deployment
Deployment and execution of analytics
Analysis accessibility to everyone
Fast and easy model deployment

Machine Learning Algorithms

Machine learning algorithms include classification algorithms, clustering algorithms, dimensionality reduction algorithms, and graph analysis

Classification

Target variable in classification is either binary or categorical
Performance metrics include true positive rate, false positive rate, positive predictive values, F1 score, area under the ROC curve

Confusion Matrix

Confusion matrix is a performance metric for binary classification
It shows the outcomes of a prediction
Outcomes include true positive, false positive, false negative, and true negative
A confusion matrix helps analyze performance

Performance Metrics: Accuracy

Accuracy is a performance metric calculated as (true positive + true negative) / total population
Other metrics include true positive rate, false negative rate, false positive rate, and true negative rate

F1 Score

F1 Score is a performance metric calculated as 2 * (PPV * TPR) / (PPV + TPR). PPV is positive predictive value and TPR is true positive rate.

Predictive Metrics

Predictive metrics include accuracy, positive predictive value, prevalence, false discovery rate, false omission rate, false negative rate, true negative rate, and true positive rate

This quiz covers advanced analytics techniques, focusing on machine learning algorithms such as classification and clustering. It explores operationalizing analytics from data insight to decision-making, highlighting the importance of model governance and accessibility. Test your knowledge on the methods and processes that drive effective analytics deployment.