Supervised Learning algorithms aim to learn from labeled data, where each data point is associated with a known output or label. The goal is to train the algorithm to make accurate predictions for new, unseen data.

Classification is a fundamental Supervised Learning task where the algorithm learns to assign input data points to predefined categories or classes based on their features.

Testing is the process of evaluating the performance of a Supervised Learning model on a dataset that was not used for training. This helps assess the model's ability to generalize to new, unseen data.

Support Vector Machines (SVM) is a powerful classification algorithm that finds the optimal hyperplane that separates data points of different classes with maximum margin.

Tuning, also known as hyperparameter optimization, involves adjusting the model's parameters to achieve the best possible performance on the training data.

Linear Regression is a widely used Supervised Learning algorithm for regression tasks, where the goal is to predict a continuous numerical value based on input features.

The training set consists of labeled data used to train the Supervised Learning model. The model learns patterns and relationships from this data to make predictions.

Accuracy is a widely used metric for evaluating classification models. It measures the proportion of correctly classified data points out of the total number of data points.

Data splitting involves dividing the available data into training, validation, and test sets. The training set is used to train the model, the validation set is used to tune the model's parameters, and the test set is used to evaluate the final model's performance.

Early stopping is a technique used to prevent overfitting in Supervised Learning models. It involves monitoring the model's performance on a validation set and stopping the training process when the model starts to overfit the training data.

Data preprocessing involves transforming the input data into a format suitable for the Supervised Learning model. This may include cleaning the data, handling missing values, and normalizing the data.

Feature selection involves selecting a subset of relevant and informative features from the input data. This can help improve the model's performance and interpretability.

Testing involves evaluating the performance of the Supervised Learning model on a dataset that was not used for training or validation. This provides an unbiased assessment of the model's ability to generalize to new, unseen data.

Logistic Regression is a widely used Supervised Learning algorithm for multi-class classification tasks. It models the probability of each class given the input features.

Deployment involves integrating the trained Supervised Learning model into a production environment to make predictions on new data. This may involve creating a web service, mobile app, or other application that uses the model to make predictions.