Select all that characterize kNN accurately.$$

It is a simpler classifier when compared to DT$.$

It is utilized to partition a given dataset to determine an accurate decision, while DT is utilized to determine similar values in a given dataset.

It can be faster, while DT should be slower with a huge dataset

It needs to read the whole dataset to predict

It doesn't provide automatic feature interaction, while DT does

It does not need training before classification.

A lot of memory is required for processing a large dataset.

It is computationally expensive because of estimating the distance between the data points for all the training samples.

There is more risk of overfitting in kNN compared to DT