Understanding Deep Belief Networks: Definition, Explanations, Examples & Code
Deep Belief Networks (DBN) is a type of deep learning algorithm that is widely used in artificial intelligence and machine learning. It is a generative graphical model with many layers of hidden causal variables, designed for unsupervised learning tasks. DBN is capable of learning rich and complex representations of data, making it well-suited for a variety of tasks in the field of AI.
Deep Belief Networks: Introduc
Understanding Density-Based Spatial Clustering of Applications with Noise: Definition, Explanations, Examples & Code
The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a clustering algorithm used in unsupervised learning. It groups together points that are densely packed (i.e. points with many nearby neighbors) and marks points as outliers if they lie alone in low-density regions. DBSCAN is commonly used in machine learning and artificial intelligence for its ability to
Understanding Differential Evolution: Definition, Explanations, Examples & Code
Differential Evolution is an optimization algorithm that aims to improve a candidate solution iteratively with respect to a defined quality measure. It belongs to the family of evolutionary algorithms and is widely used in various optimization problems, particularly in continuous and real-parameter optimization problems. Differential Evolution is a type of supervised learning method that works on the principle of na
Understanding Eclat: Definition, Explanations, Examples & Code
Eclat is an Association Rule algorithm designed for Unsupervised Learning. It is a fast implementation of the standard level-wise breadth first search strategy for frequent itemset mining.
Eclat: Introduction
Domains
Learning Methods
Type
Machine Learning
Unsupervised
Association Rule
Eclat is an algorithm used in the field of machine learning and data mining for frequent itemset mining. It is a fast implementation of
Understanding Elastic Net: Definition, Explanations, Examples & Code
Elastic Net is a regularization algorithm that is used in supervised learning. It is a powerful and efficient method that linearly combines the L1 and L2 penalties of the Lasso and Ridge methods. This combination allows for both automatic feature selection and regularization, making it particularly useful for high-dimensional datasets with collinear features.
Elastic Net: Introduction
Domains
Learning Methods
Type
Ma
Understanding Expectation Maximization: Definition, Explanations, Examples & Code
Expectation Maximization (EM) is a popular statistical technique used for finding maximum likelihood estimates of parameters in probabilistic models. This algorithm is particularly useful in cases where the model depends on unobserved latent variables. EM falls under the clustering category and is commonly used as an unsupervised learning method.
Expectation Maximization: Introduction
Domains
Learning Method
Understanding eXtreme Gradient Boosting: Definition, Explanations, Examples & Code
XGBoost, short for eXtreme Gradient Boosting, is a popular machine learning algorithm that employs the gradient boosting framework. It leverages decision trees as base learners and combines them to produce a final, more robust prediction model. Renowned for its speed and performance, XGBoost is primarily used for supervised learning tasks such as regression and classification. It is classified as an Ensemble algo
Understanding Flexible Discriminant Analysis: Definition, Explanations, Examples & Code
The Flexible Discriminant Analysis (FDA), also known as FDA, is a dimensionality reduction algorithm that is a generalization of linear discriminant analysis. Unlike the traditional linear discriminant analysis, FDA uses non-linear combinations of predictors to achieve better classification accuracy. It falls under the category of supervised learning algorithms, where it requires labeled data to build a deci
Understanding Gaussian Naive Bayes: Definition, Explanations, Examples & Code
Gaussian Naive Bayes is a variant of Naive Bayes that assumes that the likelihood of the features is Gaussian. It falls under the Bayesian type of algorithms and is used for Supervised Learning.
Gaussian Naive Bayes: Introduction
Domains
Learning Methods
Type
Machine Learning
Supervised
Bayesian
Gaussian Naive Bayes is a Bayesian algorithm that belongs to the Naive Bayes family. This algorithm is a varia
Understanding Genetic: Definition, Explanations, Examples & Code
The Genetic algorithm is a type of optimization algorithm that is inspired by the process of natural selection, and is considered a heuristic search and optimization method. It is a popular algorithm in the field of artificial intelligence and machine learning, and is used to solve a wide range of optimization problems. Genetic algorithms work by mimicking the process of natural selection, allowing for the fittest individuals to s
Understanding Gradient Boosted Regression Trees: Definition, Explanations, Examples & Code
The Gradient Boosted Regression Trees (GBRT), also known as Gradient Boosting Machine (GBM), is an ensemble machine learning technique used for regression problems.
This algorithm combines the predictions of multiple decision trees, where each subsequent tree improves the errors of the previous tree. The GBRT algorithm is a supervised learning method, where a model learns to predict an outcome variable f
Understanding Gradient Boosting Machines: Definition, Explanations, Examples & Code
The Gradient Boosting Machines (GBM) is a powerful ensemble machine learning technique used for regression and classification problems. It produces a prediction model in the form of an ensemble of weak prediction models. GBM is a supervised learning method that has become a popular choice for predictive modeling thanks to its performance and flexibility.
Gradient Boosting Machines: Introduction
Domains
Lea
Understanding Gradient Descent: Definition, Explanations, Examples & Code
Gradient Descent is a first-order iterative optimization algorithm used to find a local minimum of a differentiable function. It is one of the most popular algorithms for machine learning and is used in a wide variety of applications. Gradient Descent belongs to the broad class of learning methods that are used to optimize the parameters of models.
Gradient Descent: Introduction
Domains
Learning Methods
Type
Mac
Understanding Hierarchical Clustering: Definition, Explanations, Examples & Code
Hierarchical Clustering is a clustering algorithm that seeks to build a hierarchy of clusters. It is commonly used in unsupervised learning where there is no predefined target variable. This method of cluster analysis groups similar data points into clusters based on their distance from each other. The clusters are then merged together to form larger clusters until all data points are in a single cluster. Hierarchi
Understanding Hopfield Network: Definition, Explanations, Examples & Code
The Hopfield Network is a type of artificial neural network that serves as content-addressable memory systems with binary threshold nodes. As a recurrent neural network, it has the ability to store and retrieve patterns in a non-destructive manner. The learning methods used in Hopfield Network include both supervised and unsupervised learning.
Hopfield Network: Introduction
Domains
Learning Methods
Type
Machine
Understanding Isolation Forest: Definition, Explanations, Examples & Code
Isolation Forest is an unsupervised learning algorithm for anomaly detection that works on the principle of isolating anomalies. It is an ensemble type algorithm, which means it combines multiple models to improve performance.
Isolation Forest: Introduction
Domains
Learning Methods
Type
Machine Learning
Unsupervised
Ensemble
The Isolation Forest algorithm is an ensemble, unsupervised learning method that has
Understanding Iterative Dichotomiser 3: Definition, Explanations, Examples & Code
The Iterative Dichotomiser 3 (ID3) is a decision tree algorithm invented by Ross Quinlan used to generate a decision tree from a dataset. It is a type of supervised learning method, where the algorithm learns from a labeled dataset and creates a tree-like model of decisions and their possible consequences. The ID3 algorithm is widely used in machine learning and data mining for classification problems.
Iterative
Understanding k-Means: Definition, Explanations, Examples & Code
The k-Means algorithm is a method of vector quantization that is popular for cluster analysis in data mining. It is a clustering algorithm based on unsupervised learning.
k-Means: Introduction
Domains
Learning Methods
Type
Machine Learning
Unsupervised
Clustering
Name: k-Means
Definition: A method of vector quantization, that is popular for cluster analysis in data mining.
Type: Clustering
Learning Methods:
* Un