Select all the true statments:options:1).We can not design an ML algorithm that performs well on all class of problems2).It is not possible to learn in high dimensions due to the curse of dimensionality3).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 model4).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 set5).If the method performs equally well on train and test sets, we can assume it will have a low generalization error in practice QuestionSelect all the true statments:o We can not design an ML algorithm that performs well on all class of problemso It is not possible to lean in high dimensions due to the curse of dimensionalityo When our model has hyperparameters, we tune them using a validation set, and the bestperformance achieved on the validation set gives the generalization error of our modelo When our model has hyperparameters, we tune them using a validation set, and use the bestperformance achieved to compare our model with the performance of other baselines on thesame validation seto If the method performs equally well on train and test sets, we can assume it will have a lowgeneralization error in practice

Answered: Select all the true statments: options:…

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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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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