Logistic Regression is a popular algorithm for binary classification problems, where the output is a probability estimate of the class label.

The main objective of supervised learning is to train a model that can accurately predict output labels for new, unseen data.

Random Forest is an ensemble learning algorithm that can handle high-dimensional data effectively due to its ability to combine multiple decision trees.

In Linear Regression, the hypothesis function is a linear equation that represents the relationship between the input variables and the output variable.

Support Vector Machines are often used for multi-class classification problems due to their ability to find an optimal hyperplane that separates different classes.

Tuning involves adjusting the hyperparameters of a supervised learning model to optimize its performance on unseen data.

Neural Networks, particularly deep neural networks, are powerful models that can capture complex non-linear relationships in data.

Testing involves evaluating the performance of a supervised learning model on a dataset that was not used during training.

Linear Regression is a widely used algorithm for regression tasks, where it models the relationship between input variables and a continuous output variable.

Splitting a dataset into training, validation, and testing sets is a crucial step in supervised learning to ensure unbiased evaluation of the model.

Random Forest is robust to missing values and can handle categorical features without the need for explicit encoding.

Feature Selection involves identifying and selecting the most relevant and informative features from a dataset for supervised learning.

One-Class SVM is a variant of Support Vector Machines specifically designed for anomaly detection and outlier identification.

Regularization techniques, such as L1 and L2 regularization, are used to prevent overfitting by penalizing large coefficients in the model.

Recurrent Neural Networks, such as LSTMs and GRUs, are powerful models for time series forecasting and sequence prediction due to their ability to learn long-term dependencies.