Logistic Regression is primarily used for binary classification tasks, where the output is predicted as either 0 or 1.

Logistic Regression produces a probability value between 0 and 1, indicating the likelihood of an observation belonging to a specific class.

The sigmoid function is a crucial component of Logistic Regression, and it maps input values to a probability scale.

Cross-Entropy Loss is commonly used in Logistic Regression to measure the difference between predicted and actual probabilities.

L2 Regularization, also known as Ridge Regression, adds a penalty term to the cost function to discourage large coefficients, thus reducing overfitting.

Logistic Regression excels when the data is linearly separable, meaning a hyperplane can perfectly separate the two classes.

Activation Function is not a hyperparameter in Logistic Regression. It is a fixed function, typically the sigmoid function, that transforms the input to a probability.

Resampling techniques, such as oversampling or undersampling, can be used to balance the class distribution and improve the performance of Logistic Regression on imbalanced data.

Logistic Regression is often preferred for its interpretability. The coefficients of the model provide insights into the relationship between features and the target variable.

F1 Score is not a common evaluation metric specifically for Logistic Regression. It is more commonly used in multi-class classification problems.

Softmax Regression is a generalization of Logistic Regression for multi-class classification. It uses a softmax function to convert the output of the model into a probability distribution over multiple classes.

Image classification is typically handled by more advanced deep learning models, such as Convolutional Neural Networks (CNNs), rather than Logistic Regression.

Logistic Regression is sensitive to outliers, as they can significantly influence the model's coefficients and predictions.

Regularization techniques, such as L1 or L2 regularization, can help reduce the impact of outliers and improve the stability of Logistic Regression.

Logistic Regression does not necessarily require a large amount of data. It can perform well even with moderate-sized datasets.