Understanding Actor-critic: Definition, Explanations, Examples & Code
Actor-critic is a temporal difference algorithm used in reinforcement learning.
It consists of two networks: the actor, which decides which action to take, and the critic, which evaluates the action produced by the actor by computing the value function and informs the actor how good the action was and how it should adjust.
In simple terms, the actor-critic is a temporal difference version of policy gradient. The learning of
Understanding AdaBoost: Definition, Explanations, Examples & Code
AdaBoost is a machine learning meta-algorithm that falls under the category of ensemble methods. It can be used in conjunction with many other types of learning algorithms to improve performance.
AdaBoost uses supervised learning methods to iteratively train a set of weak classifiers and combine them into a strong classifier.
AdaBoost: Introduction
Domains
Learning Methods
Type
Machine Learning
Supervised
Ensemble
Adadelta is an optimization algorithm that falls under the category of learning methods in the field of machine learning.
It is an extension and improvement of Adagrad that adapts learning rates based on a moving window of gradient updates.
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Adadelta: Introduc
Understanding Affinity Propagation: Definition, Explanations, Examples & Code
The Affinity Propagation (AP) algorithm is a type of unsupervised machine learning algorithm used for clustering. It automatically determines the number of clusters and operates by passing messages between pairs of samples until convergence, resulting in a set of exemplars that best represent dataset samples. AP is a powerful tool for clustering and is frequently used in various applications such as image and text seg
Understanding Apriori: Definition, Explanations, Examples & Code
Apriori is an association rule algorithm used for unsupervised learning. It is designed for frequent item set mining and association rule learning over relational databases.
Apriori: Introduction
Domains
Learning Methods
Type
Machine Learning
Unsupervised
Association Rule
The Apriori algorithm is a widely used method for frequent item set mining and association rule learning over relational databases. It is a type of
Understanding Asynchronous Advantage Actor-Critic: Definition, Explanations, Examples & Code
The Asynchronous Advantage Actor-Critic (A3C) algorithm is a deep reinforcement learning method that uses multiple independent neural networks to generate trajectories and update parameters asynchronously. It involves two models: an actor, which decides which action to take, and a critic, which estimates the value of taking that action. A3C is abbreviated as A3C and falls under the category of deep lear
Understanding Averaged One-Dependence Estimators: Definition, Explanations, Examples & Code
Averaged One-Dependence Estimators, also known as AODE, is a Bayesian probabilistic classification learning technique used for supervised learning. It directly estimates the conditional probability of the class variable given the attribute variables.
Averaged One-Dependence Estimators: Introduction
Domains
Learning Methods
Type
Machine Learning
Supervised
Bayesian
Averaged One-Dependence Es
Understanding Back-Propagation: Definition, Explanations, Examples & Code
Back-Propagation is a method used in Artificial Neural Networks during Supervised Learning. It is used to calculate the error contribution of each neuron after a batch of data. This popular algorithm is used to train multi-layer neural networks and is the backbone of many machine learning models.
Back-Propagation: Introduction
Domains
Learning Methods
Type
Machine Learning
Supervised
Artificial Neural Network
Understanding Bayesian Network: Definition, Explanations, Examples & Code
The Bayesian Network (BN) is a type of Bayesian statistical model that represents a set of variables and their conditional dependencies via a directed acyclic graph. BN is a powerful tool in machine learning and artificial intelligence for modeling complex systems. In BN, variables are represented as nodes on a graph and the relationships between them are indicated by arrows connecting the nodes. BN is known for its abili
Understanding Boosting: Definition, Explanations, Examples & Code
Boosting is a machine learning ensemble meta-algorithm that falls under the category of ensemble learning methods and is mainly used to reduce bias and variance in supervised learning.
Boosting: Introduction
Domains
Learning Methods
Type
Machine Learning
Supervised
Ensemble
Boosting is a powerful ensemble meta-algorithm used in machine learning to reduce bias and variance in supervised learning. As an ensemble techn
Understanding Bootstrapped Aggregation: Definition, Explanations, Examples & Code
Bootstrapped Aggregation is an ensemble method in machine learning that improves stability and accuracy of machine learning algorithms used in statistical classification and regression. It is a supervised learning technique that builds multiple models on different subsets of the available data and then aggregates their predictions. This method is also known as bagging and is particularly useful when the base model
Understanding C5.0: Definition, Explanations, Examples & Code
C5.0 is a decision tree algorithm used for supervised learning. It is an updated version of the earlier ID3 algorithm, and is widely used to generate decision trees.
C5.0: Introduction
Domains
Learning Methods
Type
Machine Learning
Supervised
Decision Tree
C5.0 is a decision tree algorithm that is widely used in supervised learning. It is an updated version of the ID3 algorithm and is known for its high accuracy and per
Understanding CatBoost: Definition, Explanations, Examples & Code
Developed by Yandex, CatBoost (short for "Category" and "Boosting") is a machine learning algorithm that uses gradient boosting on decision trees. It is specifically designed to work effectively with categorical data by transforming categories into numbers in a way that doesn't impose arbitrary ordinality. CatBoost is an ensemble algorithm and utilizes supervised learning methods.
CatBoost: Introduction
Domains
Learning Met
Understanding Chi-squared Automatic Interaction Detection: Definition, Explanations, Examples & Code
Chi-squared Automatic Interaction Detection, commonly known as CHAID, is a decision tree technique that falls under the category of supervised learning. It is based on adjusted significance testing and is utilized to identify the most significant predictors of a particular outcome. This algorithm is a popular tool for data mining and statistical analysis, as it allows for the creation of a decis
Understanding Classification and Regression Tree: Definition, Explanations, Examples & Code
Classification and Regression Tree, also known as CART, is an umbrella term used to refer to various types of decision tree algorithms. It belongs to the category of Decision Trees and is primarily used in Supervised Learning methods.
Classification and Regression Tree: Introduction
Domains
Learning Methods
Type
Machine Learning
Supervised
Decision Tree
Classification and Regression Tree, c
Understanding Conditional Decision Trees: Definition, Explanations, Examples & Code
Conditional Decision Trees are a type of decision tree used in supervised and unsupervised learning. They are a tree-like model of decisions, where each node represents a feature, each link (branch) represents a decision rule, and each leaf represents an outcome.
Conditional Decision Trees: Introduction
Domains
Learning Methods
Type
Machine Learning
Supervised, Unsupervised
Decision Tree
Conditiona
Understanding Convolutional Neural Network: Definition, Explanations, Examples & Code
Convolutional Neural Network (CNN), a class of deep neural networks, is widely used in pattern recognition and image processing tasks. CNNs can also be applied to any type of input that can be structured as a grid, such as audio spectrograms or time-series data. They are designed to automatically and adaptively learn spatial hierarchies of features from the input data. CNNs contain convolutional layers that fi
Understanding Decision Stump: Definition, Explanations, Examples & Code
The Decision Stump is a type of Decision Tree algorithm used in Supervised Learning. It is a one-level decision tree that is often used as a base classifier in many ensemble methods.
Decision Stump: Introduction
Domains
Learning Methods
Type
Machine Learning
Supervised
Decision Tree
Decision Stump is a type of decision tree used in supervised learning. It is a one-level decision tree that acts as a base classi
