K means clustering - Samenvattingen, Aantekeningen en Examens

This two-page Data Science and Statistics (DSS) Cheat Sheet is designed to support students preparing for the INFOMDSS open book exam, providing a focused yet comprehensive overview of essential clustering, classification, and evaluation metrics. With quick-reference explanations of key concepts—such as clustering methods (K-means, DBSCAN), performance metrics (Silhouette Index, ROC curves), and data quality measures—this cheat sheet is also a valuable resource for other data science courses...

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...

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...

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. ...

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...

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 ...

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CPV 301 Final Exam | Q & A (Complete Solutions) The input of computer vision is an image. So what is the output of computer vision? A. The output is the interpretation of an image. B. The output is a processed image. C. The output is the image that has been recovered with the parts that have been noisy- D. All of the others What system does the digital camera use to perform the digitization of images? A. Number system. B. Logic system C. Sensor system D. Optical system In K-means for Segmentat...

5 steps to building a machine learning model - 1. choosing a class of model 2. choose hyperparameters 3. arrange data 4. fit the model 5. predict a silhouette score of 1 is the [best/worst] and -1 is the [best/worst] score - best, worst basic idea of regression - we have some X values called features and some Y value, the variable we are trying to predict Difference between unsupervised and supervised learning - unsupervised: you have an X but no Y supervised: you have an X and a Y Ima...