Question

23.
In Model based learning methods, an iterative process takes place on the ML
models that are built based on various model parameters, called ?
A mini-batches
B optimized parameters
C hyper parameters
D super parameters

Answer 1

Answer:

In Model based learning methods, an iterative process takes place on the ML models that are built based on various model parameters, called hyperparameters.

Explanation:

Pls mark me as brainlist

Answer 2

Answer:

In Model based learning methods, an iterative process takes place on the ML models that are built based on various model parameters, called Hyper Parameters.

Explanation:

Hyperparameters are variables whose values influence the learning process and define the model parameter values that a learning algorithm ultimately learns.

The prefix "hyper_" implies that these parameters are "top-level" controls over the learning process and the model parameters that emerge from it.

Before the model is even trained, as a machine learning engineer developing a model, you select and establish the hyperparameter values that your learning algorithm will employ.

Hyperparameters are referred to be external to the model in this context since the model is unable to alter its values during learning or training.

The learning algorithm uses hyperparameters when learning, but they are not included in the model that is produced.

We have the trained model parameters—effectively, what we refer to as the model—at the conclusion of the learning process.

This model does not include the hyperparameters that were utilised during training. We only know the model parameters that were learned; for example, we cannot determine the hyperparameter values that were used to train a model from the model itself.