K fold cross validation - Study guides, Class notes & Summaries

ISYE 6501 MIDTERM 1 COMPLETE EXAM WITH UPGRADED QUESTIONS AND ANSWERS 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...

Georgia Tech WEEK 2 HOMEWORK – SAMPLE SOLUTIONS IMPORTANT NOTE, Rated A+, 2022. Document Content and Description Below WEEK 2 HOMEWORK – SAMPLE SOLUTIONS IMPORTANT NOTE These homework solutions show multiple approaches and some optional extensions for most of the questions in the assignment. You don’t need to s ubmit all this in your assignments; they’re included here just to help you learn more – because remember, the main goal of the homework assignments, and of the entire course, ...

Which of the following is the key limitation of the simple perceptron? - It can solve only linearly separable problems In theory, a polynomial regression model of sufficient complexity is a universal approximator. (T/F)? - true Even after training is completed, neural networks are usually slow to generate their estimates/decisions. (T/F)? - false A linear perceptron is a nonlinear model. (T/F)? - false The addition of direct connections between the input and output layers...

Support Vector Machine 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 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 training points in the decision funct...

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 training poin...

GEORGIA Tech, ISYE Full course, Graded A+, 2022 update Document Content and Description Below Week 1 Why Analytics? 6 Data Vocabulary 7 Classification 8 Support Vector Machines 11 Scaling and Standardization 13 k-Nearest Neighbor (KNN) 13 Week 2 Model Validation 16 Validation and Test Sets 17 Splitting the Data 18 Cross-Validation 20 Clustering 21 Supervised vs. Unsupervised Learning 22 Week 3 Data Preparation 25 Introduction to Outliers 25 Change Detection 27 Week 4 Time Series Data 31 AutoRe...

Supervised Learning correct answers Training of a ML model using datasets with labels Unsupervised Learning correct answers Training of a ML model without labeled datasets Training Set correct answers Set of data with labels and features used to teach the ML model/determine its parameters Validation Set correct answers Set used during training to fine tune the model's parameters/determine its hyperparameters & evaluate the model's performance Testing Set correct answers Used a...

Support Vector Machine(SVM) is a supervised machine learning algorithm used for? - ANSWER Classification How to split the data if we only have one model? - ANSWER 70% training data, 30% testing data How to split the data if we want to compare models? - ANSWER 70% training, 15% validation and 15% testing When do we need to do scaling in data? - ANSWER When our factors/attributes/dimensions are orders of magnitude different such as income vs. credit score (income is much much larger) Whic...

ISYE 6501 Midterm 1 QUESTIONS CORRECTLY ANSWERED LATEST UPDATE Support Vector Machine(SVM) is a supervised machine learning algorithm used for? - ANSWER Classification How to split the data if we only have one model? - ANSWER 70% training data, 30% testing data How to split the data if we want to compare models? - ANSWER 70% training, 15% validation and 15% testing When do we need to do scaling in data? - ANSWER When our factors/attributes/dimensions are orders of magnitude differe...

Georgia Tech ISYE Midterm 1 Notes: Week 1 Classification:, Graded A+ Document Content and Description Below ISYE Midterm 1 Notes: Week 1 Classification: - Two main types of classifiers: o Hard Classifier: A classifier that perfectly separates data into 2 (or more) correct classes. This type of classifie r is rigid and is only applicable to perfectly separable datasets. o Soft Classifier: A classifier that does not perfectly separate data into perfectly correct classes. This type is used when a...