Understanding Multivariate Adaptive Regression Splines: Definition, Explanations, Examples & Code
Multivariate Adaptive Regression Splines (MARS) is a regression analysis algorithm that models complex data by piecing together simpler functions. It falls under the category of supervised learning methods and is commonly used for predictive modeling and data analysis.
Multivariate Adaptive Regression Splines: Introduction
Domains
Learning Methods
Type
Machine Learning
Supervised
Regressi
Understanding Naive Bayes: Definition, Explanations, Examples & Code
Naive Bayes is a Bayesian algorithm used in supervised learning to classify data. It is a simple probabilistic classifier that applies Bayes' theorem with strong independence assumptions between the features.
Naive Bayes: Introduction
Domains
Learning Methods
Type
Machine Learning
Supervised
Bayesian
Naive Bayes is a popular algorithm used in machine learning for classification tasks. It is a simple probabilistic
Understanding Ordinary Least Squares Regression: Definition, Explanations, Examples & Code
The Ordinary Least Squares Regression (OLSR) is a regression algorithm used in supervised learning. It is a type of linear least squares method utilized for estimating the unknown parameters in a linear regression model. As a regression algorithm, OLSR is used to predict continuous numerical values. It is widely used in various fields, including finance, economics, engineering, and social sciences, to ana
Understanding Partial Least Squares Regression: Definition, Explanations, Examples & Code
Partial Least Squares Regression (PLSR) is a dimensionality reduction technique used in supervised learning. PLSR is a method for constructing predictive models when the factors are many and highly collinear. It is a regression-based approach that seeks to find the directions in the predictor space that explain the maximum covariance between the predictors and the response.
Partial Least Squares Regressi
Understanding Particle Swarm Optimization: Definition, Explanations, Examples & Code
Particle Swarm Optimization (PSO) is an optimization algorithm inspired by the social behavior of birds and fish. It operates by initializing a swarm of particles in a search space, where each particle represents a potential solution. The particles move in the search space, guided by the best position found by the swarm and their own best position, ultimately converging towards the optimal solution. PSO is a po
Understanding Perceptron: Definition, Explanations, Examples & Code
The Perceptron is a type of Artificial Neural Network that operates as a linear classifier. It makes its predictions based on a linear predictor function combining a set of weights with the feature vector. This algorithm falls under the category of Supervised Learning methods.
Perceptron: Introduction
Domains
Learning Methods
Type
Machine Learning
Supervised
Artificial Neural Network
The Perceptron is a type of Ar
Understanding Policy Gradients: Definition, Explanations, Examples & Code
Policy Gradients (PG) is an optimization algorithm used in artificial intelligence and machine learning, specifically in the field of reinforcement learning. This algorithm operates by directly optimizing the policy the agent is using, without the need for a value function. The agent's policy is typically parameterized by a neural network, which is trained to maximize expected return.
Policy Gradients: Introduction
Understanding Principal Component Analysis: Definition, Explanations, Examples & Code
Principal Component Analysis (PCA) is a type of dimensionality reduction technique in machine learning that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. It is an unsupervised learning method commonly used in exploratory data analysis and data compression.
Principal Compo
Understanding Principal Component Regression: Definition, Explanations, Examples & Code
Principal Component Regression (PCR) is a dimensionality reduction technique that combines Principal Component Analysis (PCA) and regression. It first extracts the principal components of the predictors and then performs a linear regression on these components. PCR is a supervised learning method that can be used to improve the performance of regression models by reducing the number of predictors and removin
Understanding Projection Pursuit: Definition, Explanations, Examples & Code
Projection Pursuit is a type of dimensionality reduction algorithm that involves finding the most "interesting" possible projections in multidimensional data. It is a statistical technique that can be used for various purposes, such as data visualization, feature extraction, and exploratory data analysis. The algorithm uses a criterion function to identify the most informative projections, which can be either supervised
Understanding Quadratic Discriminant Analysis: Definition, Explanations, Examples & Code
Quadratic Discriminant Analysis (QDA) is a dimensionality reduction algorithm used for classification tasks in supervised learning. QDA generates a quadratic decision boundary by fitting class conditional densities to the data and using Bayes’ rule. As a result, QDA is a useful tool for solving classification problems with non-linear decision boundaries.
Quadratic Discriminant Analysis: Introduction
D
Understanding Radial Basis Function Network: Definition, Explanations, Examples & Code
The Radial Basis Function Network (RBFN) is a type of Artificial Neural Network that uses radial basis functions as activation functions. It is a supervised learning algorithm, which means that it requires input and output data to train the network. The RBFN is known for its ability to approximate any function to arbitrary levels of accuracy and is commonly used for function approximation, classification, and
Understanding Random Forest: Definition, Explanations, Examples & Code
Random Forest is an ensemble machine learning method that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes of the individual trees. It falls under the category of supervised learning.
Random Forest: Introduction
Domains
Learning Methods
Type
Machine Learning
Supervised
Ensemble
The Random Forest algorithm is a popular and effective
Understanding Recurrent Neural Network: Definition, Explanations, Examples & Code
The Recurrent Neural Network, also known as RNN, is a type of Deep Learning algorithm. It is characterized by its ability to form directed graph connections between nodes along a sequence, which allows it to exhibit temporal dynamic behavior. RNN has become increasingly popular in recent years due to its ability to handle sequential data of varying lengths. RNN can be trained using both Supervised and Unsupervised
Understanding Ridge Regression: Definition, Explanations, Examples & Code
Ridge Regression is a regularization method used in Supervised Learning. It uses L2 regularization to prevent overfitting by adding a penalty term to the loss function. This penalty term limits the magnitude of the coefficients in the regression model, which can help prevent overfitting and improve generalization performance.
Ridge Regression: Introduction
Domains
Learning Methods
Type
Machine Learning
Supervise
Understanding Rotation Forest: Definition, Explanations, Examples & Code
Rotation Forest is an ensemble learning method that generates individual decision trees based on differently transformed subsets of the original features. The transformations aim to enhance diversity among the individual models, increasing the robustness of the resulting ensemble model. It falls under the category of supervised learning.
Rotation Forest: Introduction
Domains
Learning Methods
Type
Machine Learning
Understanding Sammon Mapping: Definition, Explanations, Examples & Code
Sammon Mapping is a non-linear projection method used in dimensionality reduction. It belongs to the unsupervised learning methods and aims to preserve the structure of the data as much as possible in lower-dimensional spaces.
Sammon Mapping: Introduction
Domains
Learning Methods
Type
Machine Learning
Unsupervised
Dimensionality Reduction
Sammon Mapping is a dimensionality reduction algorithm that belongs to t
Understanding Semi-Supervised Support Vector Machines: Definition, Explanations, Examples & Code
Semi-Supervised Support Vector Machines (S3VM) is an extension of Support Vector Machines (SVM) for semi-supervised learning. It is an instance-based algorithm that makes use of a large amount of unlabelled data and a small amount of labelled data to perform classification tasks. The aim is to leverage the unlabelled data to improve the decision boundary constructed from the labelled data alone, whi