K means clustering Study guides, Class notes & Summaries

Data Science - ANSWER interdisciplinary field about scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured 3 Analytical Approaches to Predictive Analytics - ANSWER Statistics, Artificial Intelligence, Machine Learning Statistics - ANSWER the foundation for predictive analytics -- the building blocks Ex: regression, factor & cluster analysis Artificial Intelligence - ANSWER uses heuristics- "rules of t...

Matching models/methods to categories - CORRECT ANSWER-(cusum and pca = NONE) Select all of the following models that are designed for use with attribute/feature data (i.e., not time-series data): - CORRECT ANSWER-k-nearest-neighbor, PCA, k-means, logistic regression, linear regression, random forest, SVM's Classification models - CORRECT ANSWER-CART, k-nearest-neighbor, logistic regression, random forest, support vector machine Clustering - CORRECT ANSWER-k-means Response predictio...

ISYE 6501 - Introduction to Analytics Weeks 1 – 7 Combined Frequent Exam Questions Correctly Answered Support Vector Machine (SVM) - ANSWER Supervised learning classification tool and algorithm that seeks a dividing hyperplane for any number of dimensions can be used for regression or classification. Margin of Error (SVM) - ANSWER A small margin for error reduces your chances of misclassifying known data points but increases your chances of misclassifying unknown data points. ...

What do descriptive questions ask? What happened? (e.g., which customers are most alike) What do predictive questions ask? What will happen? (e.g., what will Google's stock price be?) Brainpower Read More 0:05 / 0:15 What do prescriptive questions ask? What action(s) would be best? (e.g., where to put traffic lights) What is a model? Real-life situation expressed as math. What do classifiers help you do? differentiate What is a soft classifier and when is it...

ISYE 6501 MIDTERM 1 EXAM 1 TESTBANK LATEST 2022-2023 WITH QUESTIONS AND ANSWERS|A+GRADE Rows - ANSWER- Data points are values in data tables Columns - ANSWER- The 'answer' for each data point (response/outcome) Structured Data - ANSWER- Quantitative, Categorical, Binary, Unrelated, Time Series Unstructured Data - ANSWER- Text Support Vector Model - ANSWER- Supervised machine learning algorithm used for both classification and regression challenges. Mostly used in classificati...

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ISYE 6501 - Final Prep – Midterm Questions With Correct Answers Types of classification models KNN SVM Types of Clustering Models K-Means Clustering Types of Response Prediction Models ARIMA CART Exponential Smoothing Linear Regression Logistic Regression Random Forest Types of Validation Method Cross Validation Types of Variation Estimate Models GARCH Models that use Time Series Data ARIMA CUSUM

QUESTIONS AND ANSWERS FOR ISYE 6501 FINAL Support Vector Machine - CORRECT ANSWER-A supervised learning, classification model. Uses extremes, or identified points in the data from which margin vectors are placed against. The hyperplane between these vectors is the classifier SVM Pros/Cons - CORRECT ANSWER-Pros: It works really well with a clear margin of separation It is effective in high dimensional spaces. It is effective in cases where the number of dimensions is greater than the ...

What do descriptive questions ask? - CORRECT ANSWER-What happened? (e.g., which customers are most alike) What do predictive questions ask? - CORRECT ANSWER-What will happen? (e.g., what will Google's stock price be?) What do prescriptive questions ask? - CORRECT ANSWER-What action(s) would be best? (e.g., where to put traffic lights) What is a model? - CORRECT ANSWER-Real-life situation expressed as math. What do classifiers help you do? - CORRECT ANSWER-differentiate What is a soft...

Support Vector Machine - CORRECT ANSWER-A supervised learning, classification model. Uses extremes, or identified points in the data from which margin vectors are placed against. The hyperplane between these vectors is the classifier SVM Pros/Cons - CORRECT ANSWER-Pros: It works really well with a clear margin of separation It is effective in high dimensional spaces. It is effective in cases where the number of dimensions is greater than the number of samples. It uses a subset of tra...