Relationship between landa and weights in logistic Regression

i don’t understand relationship between landa and weights in logistic Regression
Where did the proof come from?

Hi @Ahmed-Mostafa-Ismael great question

Lambda is the regularization term while the weights allow to make predictions, the weights are penalize using lambda to avoid overfitting.

The proof for the relationship between lambda and the weights in logistic regression comes from the optimization of the objective function. The objective function in logistic regression is typically defined as the negative log-likelihood of the data plus the regularization term, which is minimized using gradient descent or other optimization algorithms.

When the regularization term is added to the objective function, the gradient of the objective function with respect to the weights is modified to include the derivative of the regularization term. The regularization term penalizes the magnitude of the weights and encourages the model to select a simpler solution with smaller weights.

Please let me know if this answers your question