Classifier penalty, classifier solver and classifier C are the trainable hyperparameters of a Logistic Regression Classifier. These can be specified exclusively with values in Grid Search to hyper tune a Logistic Classifier.

In a logistic regression model, hyperparameters are the external configuration settings that are not learned from the data but are set prior to the training process. The main hyperparameters in logistic regression include:

Regularization parameter (C or alpha): This hyperparameter controls the strength of regularization in the model. A smaller value of C or a larger value of alpha will increase the regularization strength.
Solver: The solver is the optimization algorithm used to fit the logistic regression model. Common choices include ‘liblinear’, ‘lbfgs’, ‘newton-cg’, ‘sag’, and ‘saga’.
Max iterations: This hyperparameter determines the maximum number of iterations for the solver to converge.
Penalty: It specifies the type of regularization to be applied, either ‘l1’ (L1 regularization), ‘l2’ (L2 regularization), or ‘none’ (no regularization).
Multi_class: If the logistic regression model is used for multiclass classification, this hyperparameter defines the strategy to handle multiple classes. It can take values like ‘ovr’ (one-vs-rest) or ‘multinomial’ (softmax).
Class weight: This hyperparameter allows you to assign different weights to classes, which can be useful when dealing with imbalanced datasets.

When answering this question in an interview, you could briefly explain each hyperparameter and mention how they impact the logistic regression model. Additionally, you might discuss the importance of tuning these hyperparameters based on the specific characteristics of the data to achieve optimal model performance.