Select all the true statments options We can not design an ML algorithm that performs well on all class of problems It is not possible to learn in high dimensions due to the curse of dimensionality 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 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 If the method performs equally well on train and test sets, we can assume it will have a low generalization error in practice Question (1 point) Select all the true statments o We can not design an ML algorithm that performs well on all class of problems It is not possible to learn in high dimensions due to the curse of dimensionality o 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 o 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 o 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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Select all the true statments: options: We can not design an ML algorithm that performs well on all class of problems It is not possible

image text in transcribed Select all the true statments:

options:

	We can not design an ML algorithm that performs well on all class of problems
	It is not possible to learn in high dimensions due to the curse of dimensionality
	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
	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
	If the method performs equally well on train and test sets, we can assume it will have a low generalization error in practice

Question (1 point) Select all the true statments: o We can not design an ML algorithm that performs well on all class of problems It is not possible to learn in high dimensions due to the curse of dimensionality o 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 o 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 o If the method performs equally well on train and test sets, we can assume it will have a low generalization error in practice