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06:21, 19 March 2023 Walle talk contribs created page Convolution (Created page with "{{see also|Machine learning terms}} ==Introduction== Convolution is a mathematical operation widely used in the field of machine learning, especially in the domain of deep learning and convolutional neural networks (CNNs). The operation involves the element-wise multiplication and summation of two matrices or functions, typically an input matrix (or image) and a kernel (or filter). The primary purpose of convolution is to extract features from the input data,...")
06:21, 19 March 2023 Walle talk contribs created page Bounding box (Created page with "{{see also|Machine learning terms}} ==Bounding Box in Machine Learning== ===Definition=== A '''bounding box''' is a rectangular box used in machine learning and computer vision to represent the spatial extent of an object within an image or a sequence of images. It is generally defined by the coordinates of its top-left corner and its width and height. Bounding boxes are widely employed in object detection, localization, and tracking tasks, where the objective is...")
06:21, 19 March 2023 Walle talk contribs created page MNIST (Created page with "{{see also|Machine learning terms}} ==Introduction== The '''Modified National Institute of Standards and Technology (MNIST)''' dataset is a large collection of handwritten digits that has been widely used as a benchmark for evaluating the performance of various machine learning algorithms, particularly in the field of image recognition and computer vision. MNIST, introduced by Yann LeCun, Corinna Cortes, and Christopher J.C. Burges in 1998, has played a pivot...")
21:57, 18 March 2023 Walle talk contribs created page Wisdom of the crowd (Created page with "{{see also|Machine learning terms}} ==Wisdom of the Crowd in Machine Learning== The ''Wisdom of the Crowd'' is a phenomenon that refers to the collective intelligence and decision-making ability of a group, which often leads to more accurate and reliable outcomes than individual judgments. In the context of machine learning, this concept is employed to improve the performance of algorithms by aggregating the predictions of multiple models, a technique commonly known as [...")
21:57, 18 March 2023 Walle talk contribs created page Variable importances (Created page with "{{see also|Machine learning terms}} ==Variable Importance in Machine Learning== Variable importance, also referred to as feature importance, is a concept in machine learning that quantifies the relative significance of individual variables, or features, in the context of a given predictive model. The primary goal of assessing variable importance is to identify and understand the most influential factors in a model's decision-making process. This information can be us...")
21:57, 18 March 2023 Walle talk contribs created page Threshold (for decision trees) (Created page with "{{see also|Machine learning terms}} ==Threshold in Decision Trees== In the field of machine learning, a decision tree is a widely used model for representing hierarchical relationships between a set of input features and a target output variable. The decision tree is composed of internal nodes, which test an attribute or feature, and leaf nodes, which represent a class or output value. The threshold is a critical parameter in decision tree algorithms that determines...")
21:56, 18 March 2023 Walle talk contribs created page Splitter (Created page with "{{see also|Machine learning terms}} ==Splitter in Machine Learning== A '''splitter''' in the context of machine learning refers to a method or technique used to divide a dataset into subsets, typically for the purposes of training, validation, and testing. The process of splitting data helps to prevent overfitting, generalizes the model, and provides a more accurate evaluation of a model's performance. Various techniques exist for splitting data, such as k-fold cross-val...")
21:56, 18 March 2023 Walle talk contribs created page Split (Created page with "{{see also|Machine learning terms}} ==Overview== In machine learning, the term ''split'' generally refers to the process of dividing a dataset into two or more non-overlapping parts, typically for the purposes of training, validation, and testing a machine learning model. These distinct subsets enable the evaluation and fine-tuning of model performance, helping to prevent overfitting and allowing for an unbiased estimation of the model's ability to generalize to unse...")
21:56, 18 March 2023 Walle talk contribs created page Shrinkage (Created page with "{{see also|Machine learning terms}} ==Introduction== '''Shrinkage''' in machine learning is a regularization technique that aims to prevent overfitting in statistical models by adding a constraint or penalty to the model's parameters. Shrinkage methods reduce the complexity of the model by pulling its coefficient estimates towards zero, leading to more robust and interpretable models. Popular shrinkage methods include Ridge Regression and Lasso Regression. ==Shrinka...")
21:56, 18 March 2023 Walle talk contribs created page Sampling with replacement (Created page with "{{see also|Machine learning terms}} ==Sampling with Replacement in Machine Learning== In machine learning, sampling with replacement refers to a statistical technique used for selecting samples from a given dataset or population during the process of model training or evaluation. This method allows for a sample to be selected multiple times, as each time it is drawn, it is returned to the pool of possible samples. In this article, we will discuss the implications of samp...")
21:56, 18 March 2023 Walle talk contribs created page Root (Created page with "{{see also|Machine learning terms}} ==Root in Machine Learning== The term "root" in machine learning may refer to different concepts, depending on the context in which it is being used. Two of the most common meanings are related to decision trees and the root mean square error (RMSE) in regression models. ===Decision Trees=== In the context of decision trees, the root refers to the starting point of the tree, where the first split or decision is made. Decision trees ar...")
21:56, 18 March 2023 Walle talk contribs created page Random forest (Created page with "{{see also|Machine learning terms}} ==Introduction== Random Forest is a versatile and powerful ensemble learning method used in machine learning. It is designed to improve the accuracy and stability of predictions by combining multiple individual decision trees, each of which is trained on a random subset of the available data. This technique helps to overcome the limitations of a single decision tree, such as overfitting and high variance, while preserving the b...")
21:55, 18 March 2023 Walle talk contribs created page Policy (Created page with "{{see also|Machine learning terms}} ==Policy in Machine Learning== In the field of machine learning, a policy refers to a decision-making function that maps a given state or input to an action or output. A policy is often denoted by the symbol π (pi) and is central to the process of learning and decision-making in various machine learning algorithms, particularly in the realm of reinforcement learning. ===Reinforcement Learning and Policies=== Reinforcement lea...")
21:55, 18 March 2023 Walle talk contribs created page Permutation variable importances (Created page with "{{see also|Machine learning terms}} ==Permutation Variable Importance== Permutation Variable Importance (PVI) is a technique used in machine learning to evaluate the importance of individual features in a predictive model. This method estimates the impact of a specific feature on the model's predictive accuracy by assessing the changes in model performance when the values of that feature are permuted randomly. The main advantage of PVI is its applicability to a wide...")
21:55, 18 March 2023 Walle talk contribs created page Greedy policy (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning and reinforcement learning, a '''greedy policy''' is a decision-making strategy that selects the action with the highest immediate value or reward, without considering the long-term consequences or future states. This approach can be effective in specific scenarios, but may fail to achieve optimal solutions in complex environments. This article will discuss the concept of greedy policy,...")
21:55, 18 March 2023 Walle talk contribs created page Experience replay (Created page with "{{see also|Machine learning terms}} ==Introduction== Experience Replay is a technique used in machine learning, particularly in reinforcement learning, to improve the efficiency and stability of the learning process. It is widely used in algorithms such as Deep Q-Network (DQN), Asynchronous Advantage Actor-Critic (A3C), and other deep reinforcement learning methods. Experience Replay allows the agent to store past experiences in a memory buffer and then reuse the...")
21:55, 18 March 2023 Walle talk contribs created page Epsilon greedy policy (Created page with "{{see also|Machine learning terms}} ==Introduction== The '''Epsilon-Greedy Policy''' is a widely used exploration-exploitation strategy in Reinforcement Learning (RL) algorithms. It helps balance the decision-making process between exploring new actions and exploiting the knowledge acquired thus far in order to maximize the expected cumulative rewards. ==Exploration and Exploitation Dilemma== In the context of RL, an agent interacts with an environment and learns an...")
21:55, 18 March 2023 Walle talk contribs created page Episode (Created page with "{{see also|Machine learning terms}} ==Episode in Machine Learning== An '''episode''' in machine learning refers to a sequence of steps or interactions that an agent goes through within an environment. It is a fundamental concept in the field of Reinforcement Learning (RL), where the learning process relies on trial and error. The term "episode" describes the process from the initial state until a termination condition is reached, often involving the completion of a t...")
21:54, 18 March 2023 Walle talk contribs created page Environment (Created page with "{{see also|Machine learning terms}} ==Environment in Machine Learning== The environment in machine learning is a term that refers to the contextual setting, data, and external factors that influence the training, performance, and evaluation of a machine learning algorithm. It includes a wide range of aspects, such as the type of data used, data preprocessing techniques, and the problem domain. ==Data Types and Sources== ===Structured Data=== Structured data is informati...")
21:54, 18 March 2023 Walle talk contribs created page Critic (Created page with "{{see also|Machine learning terms}} ==Critic in Machine Learning== In machine learning, a critic refers to a component or model that evaluates and provides feedback on the performance of another model, typically a learning agent. The term is commonly associated with reinforcement learning and actor-critic methods, where it is used to estimate the value function or provide a performance gradient for the learning agent. ===Reinforcement Learning and Critic=== Re...")
21:54, 18 March 2023 Walle talk contribs created page Q-learning (Created page with "{{see also|Machine learning terms}} ==Introduction== '''Q-learning''' is a model-free, reinforcement learning algorithm in the field of machine learning. The algorithm aims to train an agent to make optimal decisions in a given environment by learning the best action-selection policy. Q-learning is particularly well-suited for problems with a large state-action space and is widely used in robotics, control systems, and game playing. ==Background== ===Reinforcement L...")
21:54, 18 March 2023 Walle talk contribs created page Q-function (Created page with "{{see also|Machine learning terms}} ==Q-function in Machine Learning== The Q-function, also known as the state-action value function or simply Q-value, is a fundamental concept in the field of Reinforcement Learning (RL). It represents the expected cumulative reward an agent will receive from a specific state by taking a certain action and then following a given policy. Mathematically, the Q-function is denoted as Q(s, a), where 's' represents the state and 'a' repre...")
21:54, 18 March 2023 Walle talk contribs created page Markov property (Created page with "{{see also|Machine learning terms}} ==Introduction== The '''Markov property''' is a fundamental concept in the fields of probability theory, statistics, and machine learning. It is named after the Russian mathematician Andrey Markov, who first formalized the idea in the early 20th century. The Markov property describes a stochastic process, where the future state of a system depends only on its current state and not on its previous history. ==Markov Chains== ===Defi...")
21:54, 18 March 2023 Walle talk contribs created page Markov decision process (MDP) (Created page with "{{see also|Machine learning terms}} ==Markov Decision Process (MDP)== Markov Decision Process (MDP) is a mathematical model in machine learning and decision theory, used for modeling decision-making problems in stochastic environments. MDPs provide a formal framework for decision-making under uncertainty, taking into account the probabilistic nature of state transitions, the rewards or penalties associated with actions, and the influence of the decision-maker's choices o...")
21:53, 18 March 2023 Walle talk contribs created page Deep Q-Network (DQN) (Created page with "{{see also|Machine learning terms}} ==Introduction== In machine learning, '''Deep Q-Network (DQN)''' is an algorithm that combines the concepts of deep learning and reinforcement learning to create a robust and efficient model for solving complex problems. The DQN algorithm, introduced by researchers at DeepMind in 2013<ref>{{cite journal |title=Playing Atari with Deep Reinforcement Learning |author=Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Io...")
21:53, 18 March 2023 Walle talk contribs created page DQN (Created page with "{{see also|Machine learning terms}} ==Overview== The '''Deep Q-Network''' ('''DQN''') is an advanced model-free, online, off-policy reinforcement learning (RL) technique that combines the strengths of both deep neural networks and Q-learning. DQN was proposed by Volodymyr Mnih, et al. in their 2015 paper Playing Atari with Deep Reinforcement Learning. The primary motivation behind DQN was to address the challenges of high-dimensional...")
21:53, 18 March 2023 Walle talk contribs created page Bellman equation (Created page with "{{see also|Machine learning terms}} ==Bellman Equation in Machine Learning== The Bellman equation, named after its inventor Richard Bellman, is a fundamental concept in the field of reinforcement learning (RL), a subdomain of machine learning. The equation describes the optimal value function, which is a key element in solving many sequential decision-making problems. The Bellman equation serves as the foundation for various RL algorithms, including value iteration, poli...")
19:04, 18 March 2023 Walle talk contribs created page Word embedding (Created page with "{{see also|Machine learning terms}} ==Word Embedding in Machine Learning== Word embedding is a technique used in natural language processing (NLP), a subfield of machine learning, which focuses on enabling machines to understand, interpret, and generate human languages. Word embedding refers to the process of representing words in a numerical format, specifically as high-dimensional vectors in a continuous vector space. These vector representations capture the semantic m...")
19:04, 18 March 2023 Walle talk contribs created page Unidirectional language model (Created page with "{{see also|Machine learning terms}} ==Unidirectional Language Model== A unidirectional language model is a type of language model used in machine learning, specifically within the field of natural language processing (NLP). These models are designed to process and generate human-like text based on the input data they are provided. They function by estimating the probability of a word or token occurring within a given context, only taking into account the precedin...")
19:04, 18 March 2023 Walle talk contribs created page Unidirectional (Created page with "{{see also|Machine learning terms}} ==Unidirectional Models in Machine Learning== In the field of machine learning, unidirectional models refer to a specific class of algorithms that process input data in a single direction, from the beginning to the end. These models, in contrast to bidirectional models, do not possess the ability to consider information from later portions of the input data while processing earlier parts. Unidirectional models are particularly rele...")
19:04, 18 March 2023 Walle talk contribs created page Trigram (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning and natural language processing (NLP), a '''trigram''' is a continuous sequence of three items from a given sample of text or speech. Trigrams are a type of n-gram, where ''n'' represents the number of items in the sequence. N-grams are used in various language modeling and feature extraction tasks to analyze and predict text data. ==Language Modeling== ===Probability Estimatio...")
19:04, 18 March 2023 Walle talk contribs created page Token (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, a '''token''' refers to a fundamental unit of text or data that is used for processing, analysis, or modeling. Tokens are essential components of natural language processing (NLP) systems, which aim to enable computers to understand, interpret, and generate human language. In this context, a token can represent a single word, a character, a subword, or any other unit of text that serve...")
19:03, 18 March 2023 Walle talk contribs created page Out-of-bag evaluation (OOB evaluation) (Created page with "{{see also|Machine learning terms}} ==Out-of-Bag Evaluation== Out-of-Bag (OOB) evaluation is a model validation technique commonly used in ensemble learning methods, particularly in bagging algorithms such as Random Forests. The main idea behind OOB evaluation is to use a portion of the training data that was not used during the construction of individual base learners, for the purpose of estimating the performance of the ensemble without resorting to a separ...")
19:03, 18 March 2023 Walle talk contribs created page Oblique condition (Created page with "{{see also|Machine learning terms}} ==Oblique Condition in Machine Learning== The oblique condition refers to a specific type of decision boundary used in machine learning algorithms, particularly in classification tasks. Decision boundaries are mathematical functions or models that separate different classes or categories in the input data. Oblique decision boundaries are characterized by their non-orthogonal orientation, allowing for more complex and flexible separatio...")
19:03, 18 March 2023 Walle talk contribs created page Non-binary condition (Created page with "{{see also|Machine learning terms}} ==Introduction== In the context of machine learning, the term "non-binary condition" refers to a situation where the output or target variable of a predictive model is not restricted to two distinct classes or labels. This contrasts with binary classification tasks, where the goal is to predict one of two possible outcomes. Non-binary conditions arise in various types of problems, such as multi-class classification, multi-label classif...")
19:03, 18 March 2023 Walle talk contribs created page Node (decision tree) (Created page with "{{see also|Machine learning terms}} ==Definition== In machine learning, a '''node''' refers to a point within a decision tree at which a decision is made based on the input data. Decision trees are hierarchical, tree-like structures used to model decisions and their possible consequences, including the chance event outcomes, resource costs, and utility. Nodes in decision trees can be of three types: root node, internal node, and leaf node. ===Root Node=== The ''...")
19:03, 18 March 2023 Walle talk contribs created page Leaf (Created page with "{{see also|Machine learning terms}} ==Introduction== In machine learning, a '''leaf''' is an essential component of decision tree-based algorithms, such as decision trees, random forests, and gradient boosting machines. A leaf, also known as a terminal node, is the endpoint of a branch in a decision tree, which is used to make predictions based on a set of input features. In this article, we will discuss the concept of leaves, their role in decision tree-...")
19:03, 18 March 2023 Walle talk contribs created page Information gain (Created page with "{{see also|Machine learning terms}} ==Information Gain in Machine Learning== Information gain is a crucial concept in the field of machine learning, particularly when dealing with decision trees and feature selection. It is a metric used to measure the decrease in uncertainty or entropy after splitting a dataset based on a particular attribute. The primary goal of information gain is to identify the most informative attribute, which can be used to construct an effect...")
19:03, 18 March 2023 Walle talk contribs created page Inference path (Created page with "{{see also|Machine learning terms}} ==Inference Path in Machine Learning== The '''inference path''' in machine learning refers to the process of applying a trained model to new, unseen data in order to make predictions or decisions. This process is critical in realizing the practical applications of machine learning models, as it enables them to generalize their learned knowledge to real-world situations. ==Training and Inference Phases== Machine learning models typical...")
19:02, 18 March 2023 Walle talk contribs created page In-set condition (Created page with "{{see also|Machine learning terms}} ==In-set Condition in Machine Learning== The in-set condition is a concept in the field of machine learning that refers to the circumstance in which the training data used to train a machine learning model is representative of the data distribution that the model will encounter during real-world applications. This concept is related to the generalization performance of a model, which refers to its ability to perform well on unseen...")
19:02, 18 March 2023 Walle talk contribs created page Gradient boosting (Created page with "{{see also|Machine learning terms}} ==Introduction== Gradient boosting is a popular and powerful machine learning algorithm used for both classification and regression tasks. It belongs to the family of ensemble learning methods, which combine the predictions of multiple base models to produce a more accurate and robust prediction. The main idea behind gradient boosting is to sequentially add weak learners (typically decision trees) to the ensemble, each...")
19:02, 18 March 2023 Walle talk contribs created page Gradient boosted (decision) trees (GBT) (Created page with "{{see also|Machine learning terms}} ==Introduction== Gradient Boosted Trees (GBT), also known as Gradient Boosted Decision Trees or Gradient Boosting Machines, is a powerful ensemble learning technique in the field of machine learning. GBT constructs an ensemble of weak learners, typically decision trees, in a sequential manner, with each tree optimizing the model's performance by minimizing the error made by the previous tree. The technique is particularly well-suited f...")
19:02, 18 March 2023 Walle talk contribs created page Gini impurity (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, Gini impurity is a metric used to measure the impurity or disorder within a dataset. It is commonly employed in decision tree algorithms, such as the Classification and Regression Tree (CART) algorithm, to decide the best splitting points for nodes. The Gini impurity index quantifies the probability of misclassification by calculating the degree of purity in a dataset, which he...")
19:02, 18 March 2023 Walle talk contribs created page Feature importances (Created page with "{{see also|Machine learning terms}} ==Introduction== Feature importances refer to the quantification of the relative contribution of each feature (or input variable) to the overall predictive performance of a machine learning model. Identifying and understanding the importance of features in a model can aid in model interpretation, feature selection, and ultimately, the improvement of model performance. Various techniques have been proposed to assess the significance...")
19:02, 18 March 2023 Walle talk contribs created page Entropy (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, entropy is a fundamental concept that is derived from information theory. It is used to measure the impurity or randomness in a set of data. Entropy has various applications in machine learning, such as decision tree construction, feature selection, and information gain calculation. Understanding entropy and its implications is essential for designing and implementing effective mac...")
19:01, 18 March 2023 Walle talk contribs created page Decision tree (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, a '''decision tree''' is a popular and widely used model that helps in making predictions based on a series of decisions. The decision tree model can be used for both classification and regression tasks, and it works by recursively splitting the input data into subsets based on the values of the input features, ultimately making a prediction. ==Structure of a Decision Tree== ===No...")
19:01, 18 March 2023 Walle talk contribs created page Decision forest (Created page with "{{see also|Machine learning terms}} ==Introduction== A '''decision forest''' (also known as a '''random forest''') is an ensemble learning method in machine learning that combines multiple decision trees to generate a more accurate and robust prediction model. This method is widely used in classification and regression tasks, and it can handle both categorical and numerical input features. Decision forests are known for their ability to mitigate overfitting and improve g...")
19:01, 18 March 2023 Walle talk contribs created page Condition (Created page with "{{see also|Machine learning terms}} ==Introduction== In machine learning, the term "condition" typically refers to a criterion or a set of criteria that must be met for a specific event to occur or an action to be taken. Conditions are used in various aspects of machine learning, including decision trees, rule-based systems, and optimization algorithms. This article aims to provide an understanding of conditions in machine learning and their significance, as well as...")
19:01, 18 March 2023 Walle talk contribs created page Binary condition (Created page with "{{see also|Machine learning terms}} ==Binary Condition in Machine Learning== In the field of machine learning, a '''binary condition''' refers to a specific type of classification problem where the target variable consists of only two distinct classes or categories. These types of problems are often encountered in various applications, such as spam detection, medical diagnosis, and sentiment analysis. The primary goal of binary classification models is to correctly p...")
19:01, 18 March 2023 Walle talk contribs created page Bagging (Created page with "{{see also|Machine learning terms}} ==Bagging in Machine Learning== Bagging, or '''Bootstrap Aggregating''', is a popular ensemble learning technique in machine learning that aims to improve the stability and accuracy of a base learning algorithm by training multiple instances of the same model on different subsamples of the training data. The predictions from the individual models are then combined, usually by means of a majority vote, to produce the final output. This...")