Table of Contents
Classification
Classification is a fundamental task in supervised machine learning where the goal is to predict the categorical class label of new observations based on past observations with known labels.
In classification tasks, the data consists of instances (or samples) with features (or attributes) and corresponding class labels. The task is to build a model that learns the relationship between the features and the class labels from the training data. Once the model is trained, it can be used to predict the class labels of new, unseen instances.
- Logistic Regression: Logistic regression is a linear classification algorithm that models the probability that an instance belongs to a particular class. It uses the logistic function to ensure that the predicted probabilities fall between 0 and 1, making it suitable for binary classification tasks. Despite its name, logistic regression is used for classification rather than regression, and it's valued for its simplicity and interpretability.
- K-Nearest Neighbors (K-NN): K-Nearest Neighbors is a simple and intuitive classification algorithm that classifies an instance based on the majority class of its K nearest neighbors in the feature space. KNN is non-parametric and lazy, meaning it does not learn a model during training but instead stores all training instances and performs classification at prediction time.
- Naive Bayes: Naive Bayes is a probabilistic classification algorithm based on Bayes' theorem and the assumption of independence between features. Despite its simplifying assumption (naive independence), Naive Bayes often performs well in practice, especially for text classification and other high-dimensional datasets.
- Support Vector Regressor: Support Vector Machines are powerful classification algorithms that find the optimal hyperplane that separates different classes in the feature space. SVM aims to maximize the margin between the classes, making it robust to outliers and noise in the data. It can handle both linear and nonlinear classification tasks using different kernel functions.
