Select all the true statments: options: 1). We can not design an ML algorithm that performs well on all class of problems 2). It is not possible to learn in high dimensions due to the curse of dimensionality 3). When our model has hyperparameters, we tune them using a validation set, and the best performance achieved on the validation set gives the generalization error of our model 4). When our model has hyperparameters, we tune them using a validation set, and use the best performance achieved to compare our model with the performance of other baselines on the same validation set 5). If the method performs equally well on train and test sets, we can assume it will have a low generalization error in practice
Select all the true statments: options: 1). We can not design an ML algorithm that performs well on all class of problems 2). It is not possible to learn in high dimensions due to the curse of dimensionality 3). When our model has hyperparameters, we tune them using a validation set, and the best performance achieved on the validation set gives the generalization error of our model 4). When our model has hyperparameters, we tune them using a validation set, and use the best performance achieved to compare our model with the performance of other baselines on the same validation set 5). If the method performs equally well on train and test sets, we can assume it will have a low generalization error in practice
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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Question
Select all the true statments:
options:
1).
We can not design an ML algorithm that performs well on all class of problems
2).
It is not possible to learn in high dimensions due to the curse of dimensionality
3).
When our model has hyperparameters, we tune them using a validation set, and the best performance achieved on the validation set gives the generalization error of our model
4).
When our model has hyperparameters, we tune them using a validation set, and use the best performance achieved to compare our model with the performance of other baselines on the same validation set
5).
If the method performs equally well on train and test sets, we can assume it will have a low generalization error in practice
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