Category: Data Science ENG

The Naive Bayes algorithm is a probabilistic classifier based on Bayes’ theorem. It is often used for classification problems, where the goal is to assign a class or category to a set of data based on certain characteristics. The “naive” approach derives from the assumption of conditional independence of characteristics, which simplifies the calculation of probabilities.

The K-Nearest Neighbor algorithm is a supervised learning method used for classification and regression. The main idea is to classify a data point based on the majority of labels of its k nearest instances in the training set. “Closeness” is often measured using Euclidean distance.

Reinforcement Learning is a machine learning paradigm in which an agent learns to make sequential decisions by interacting with an environment. Unlike Supervised Learning, there are no labeled input/output pairs for training. Instead, the agent learns to perform actions through trial and error, receiving a reward or punishment signal based on her actions.

Unsupervised learning is a category of machine learning algorithms in which the model is trained on unlabeled data, without having explicit information on the desired outcome. The goal is to have the model find structures or patterns in the data on its own without being driven by desired outputs.

Logistic regression is a type of regression model used for binary classification problems, where the goal is to predict which of two classes an instance belongs to. Unlike linear regression, which predicts continuous values, logistic regression predicts probabilities that vary between 0 and 1. This is achieved by using a logistic (or sigmoid) function to transform the linear output into probabilities.

Support Vector Regression (SVR) is a regression technique that is based on the concept of Support Vector Machines (SVM) and aims to find a function that approximates the training data by minimizing the error, but also allowing a certain amount of error within a specified margin.

Machine Learning (ML) is a field of artificial intelligence (AI) that focuses on developing algorithms and models that allow computers to learn from data and improve their performance over time without being explicitly programmed. This approach is based on the idea that computers can learn from data, detecting patterns, relationships and regularities, and then apply that knowledge to new data without explicit programming.

Supervised Learning is a machine learning paradigm in which a model is trained on a labeled training dataset. Each example in the training set consists of a pair of input and associated output, where the output is the “correct answer” or label provided by the supervisor.