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1. In hold-out cross validation, the dataset is split to 80% training set and 20% cross validation set. 2. k-fold cross validation takes much time in training data than using hold-out. 3. Generalization is the process of predicting seen inputs.

1. In hold-out cross validation, the dataset is split to 80% training set and 20% cross validation set. 2. k-fold cross validation takes much time in training data than using hold-out. 3. Generalization is the process of predicting seen inputs.

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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Q3) Answer the following ONLY TEN with True or False, and state why if it is false: 1. In hold-out cross validation, the dataset is split to 80% training set and 20% cross validation set. 2. k-fold cross validation takes much time in training data than using hold-out. 3. Generalization is the process of predicting seen inputs. 4. In ANN, the main objective of the learning process is to decrease the error (delta). 5. Text data is directly fed into the input layer of ANN. 6. In KNN, if k equals 1 then the model suffers from underfitting. 7. Clustering is an example of supervised learning. 8. Learning rate is a hyper-parameter. 9. Probability of Y given X is written as P(Y|X). 10.Feature Scaling can affect the accuracy of a KNN classifier. 11. Forward Feature Selection is an example of Filter method. 12. In linear regression, the goal is to predict the weights that minimize the cost function.
Transcribed Image Text:Q3) Answer the following ONLY TEN with True or False, and state why if it is false: 1. In hold-out cross validation, the dataset is split to 80% training set and 20% cross validation set. 2. k-fold cross validation takes much time in training data than using hold-out. 3. Generalization is the process of predicting seen inputs. 4. In ANN, the main objective of the learning process is to decrease the error (delta). 5. Text data is directly fed into the input layer of ANN. 6. In KNN, if k equals 1 then the model suffers from underfitting. 7. Clustering is an example of supervised learning. 8. Learning rate is a hyper-parameter. 9. Probability of Y given X is written as P(Y|X). 10.Feature Scaling can affect the accuracy of a KNN classifier. 11. Forward Feature Selection is an example of Filter method. 12. In linear regression, the goal is to predict the weights that minimize the cost function.
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