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01:16, 20 March 2023 Walle talk contribs created page False negative rate (Created page with "{{see also|Machine learning terms}} ==Definition== The '''false negative rate''' (Type II error) in machine learning refers to the proportion of positive instances that the algorithm incorrectly classifies as negative. This is an important metric when evaluating the performance of machine learning models, particularly when assessing the capability of the model to accurately identify positive cases. The false negative rate is complementary to the sensitivity (re...")
01:16, 20 March 2023 Walle talk contribs created page Fairness metric (Created page with "{{see also|Machine learning terms}} ==Fairness Metric in Machine Learning== In the field of machine learning, fairness is an increasingly important consideration. The concept of fairness relates to the equitable treatment of different groups by algorithms and the avoidance of discriminatory outcomes. Fairness metrics are quantitative measures that help assess the fairness of a machine learning model, thus allowing researchers and practitioners to mitigate potential biase...")
01:16, 20 March 2023 Walle talk contribs created page Fairness constraint (Created page with "{{see also|Machine learning terms}} ==Fairness Constraint in Machine Learning== Machine learning is a subfield of artificial intelligence that focuses on the development of algorithms that enable computers to learn from and make predictions or decisions based on data. In the context of machine learning, fairness is an important ethical consideration, as it pertains to the equitable treatment of different individuals or groups by the algorithm. Fairness constraint...")
01:16, 20 March 2023 Walle talk contribs created page Experimenter's bias (Created page with "{{see also|Machine learning terms}} ==Experimenter's Bias in Machine Learning== Experimenter's bias, also known as researcher bias or confirmation bias, is a phenomenon that occurs when researchers unintentionally influence the outcomes of their studies or experiments to align with their expectations or preconceived beliefs. In the context of machine learning, experimenter's bias can manifest in various stages of the development process, including data collection, prepro...")
01:16, 20 March 2023 Walle talk contribs created page Equalized odds (Created page with "{{see also|Machine learning terms}} ==Equalized Odds in Machine Learning== Equalized Odds is a fairness criterion in machine learning, which aims to mitigate discriminatory outcomes that may arise from the use of algorithms in various applications. This criterion focuses on ensuring that the error rates for different demographic groups are equal, in order to avoid biased decision-making. In the following sections, we will delve into the definition, motivation, and implem...")
01:16, 20 March 2023 Walle talk contribs created page Equality of opportunity (Created page with "{{see also|Machine learning terms}} ==Equality of Opportunity in Machine Learning== Equality of opportunity in machine learning refers to the design, implementation, and assessment of algorithms and models that ensure fairness and unbiased outcomes for different subgroups within a given population. This is particularly important when these models are used to make decisions that may have significant impacts on people's lives, such as job applications, loan approvals, or m...")
01:16, 20 March 2023 Walle talk contribs created page Ensemble (Created page with "{{see also|Machine learning terms}} ==Ensemble Methods in Machine Learning== Ensemble methods are a group of techniques in machine learning that combine the predictions of multiple models, or "base learners," to improve overall predictive performance. The idea behind ensemble methods is that the aggregation of the predictions of several individual models can lead to a more robust and accurate result than any single model alone. ===Types of Ensemble Methods=== There...")
01:15, 20 March 2023 Walle talk contribs created page Empirical risk minimization (ERM) (Created page with "{{see also|Machine learning terms}} ==Empirical Risk Minimization (ERM)== Empirical Risk Minimization (ERM) is a fundamental concept in the field of machine learning and statistical learning theory. ERM is a strategy that aims to minimize the risk of making incorrect predictions by selecting the best hypothesis from a given hypothesis set. The risk is defined as the expected loss incurred when using the selected hypothesis to make predictions on unseen data. ERM...")
01:15, 20 March 2023 Walle talk contribs created page Earth mover's distance (EMD) (Created page with "{{see also|Machine learning terms}} ==Introduction== The '''Earth Mover's Distance''' (EMD), also known as the '''Wasserstein distance''' or '''Mallows distance''', is a measure of dissimilarity between two probability distributions in machine learning, statistics, and computer vision. It was originally introduced by Y. Rubner, C. Tomasi, and L.J. Guibas in their 1998 paper titled "A Metric for Distributions with Applications to Image Databases". EMD is especially useful...")
01:15, 20 March 2023 Walle talk contribs created page GAN (Created page with "{{see also|Machine learning terms}} ==Generative Adversarial Networks (GANs)== Generative Adversarial Networks, or GANs, are a class of machine learning models introduced by Ian Goodfellow and his colleagues in 2014. GANs consist of two neural networks, a generator and a discriminator, which are trained simultaneously in a process of competing against each other. GANs have been widely used in various applications, including image synthesis, data augmentation, and sem...")
01:15, 20 March 2023 Walle talk contribs created page Estimator (Created page with "{{see also|Machine learning terms}} ==Estimator in Machine Learning== In the context of machine learning, an '''estimator''' is an algorithm or function that approximates a target function or model based on a set of input data. The primary goal of an estimator is to make predictions or infer properties of an unknown function using observed data. Estimators can be broadly categorized into two types: '''parametric''' and '''non-parametric'''. ==Parametric Estimators== Par...")
19:17, 19 March 2023 Walle talk contribs created page Eager execution (Created page with "{{see also|Machine learning terms}} ==Introduction== Eager execution is a programming paradigm in machine learning that offers a more intuitive and flexible way of building, training, and debugging computational graphs. Unlike the traditional graph-based execution, which requires the construction of a static computation graph before running any operations, eager execution allows operations to be executed immediately as they are called, similar to standard Python programs...")
19:17, 19 March 2023 Walle talk contribs created page Dropout regularization (Created page with "{{see also|Machine learning terms}} ==Dropout Regularization in Machine Learning== Dropout regularization is a technique used in machine learning to prevent overfitting in neural networks. Overfitting occurs when a model learns to perform well on the training data but fails to generalize to unseen data. This article discusses the concept of dropout regularization, its implementation, and its advantages in the context of neural networks. ===Concept=== Dropout regularizat...")
19:17, 19 March 2023 Walle talk contribs created page Disparate treatment (Created page with "{{see also|Machine learning terms}} ==Disparate Treatment in Machine Learning== Disparate treatment in machine learning refers to the unjust or prejudicial treatment of individuals or groups based on certain attributes, such as race, gender, or age, in the context of algorithmic decision-making systems. This phenomenon occurs when the model learns to make biased decisions due to the presence of discriminatory patterns in the training data, resulting in unfair treatment f...")
19:17, 19 March 2023 Walle talk contribs created page Disparate impact (Created page with "{{see also|Machine learning terms}} ==Disparate Impact in Machine Learning== Disparate impact in machine learning refers to the unintended and potentially discriminatory consequences of an algorithmic decision-making process, where certain groups or individuals may be adversely affected due to biases in the data or model. This phenomenon raises significant ethical, legal, and social concerns, as it may perpetuate or exacerbate existing inequalities. ===Causes of Dispara...")
19:16, 19 March 2023 Walle talk contribs created page Discriminator (Created page with "{{see also|Machine learning terms}} ==Introduction== A '''discriminator''' in the context of machine learning refers to a model or a component of a model designed to distinguish between different types of data. Discriminators are most commonly used in Generative Adversarial Networks (GANs), where they play a crucial role in the training process by evaluating the authenticity of generated data samples. This article provides an overview of discriminators, their applica...")
19:16, 19 March 2023 Walle talk contribs created page Discriminative model (Created page with "{{see also|Machine learning terms}} ==Discriminative Models in Machine Learning== Discriminative models are a class of machine learning algorithms that aim to model the decision boundary between different classes or categories. These models focus on estimating the conditional probability of a class label given a set of input features, denoted as P(Y|X), where Y represents the class label and X the input features. Discriminative models are widely used for various tasks, s...")
19:16, 19 March 2023 Walle talk contribs created page Dimensions (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, dimensions refer to the number of features or attributes used to represent data points in a dataset. High-dimensional data can pose challenges to traditional machine learning algorithms, while also providing opportunities for more complex and detailed analyses. This article will explore the concept of dimensions in machine learning, their implications, and strategies for dealing wi...")
19:16, 19 March 2023 Walle talk contribs created page Dimension reduction (Created page with "{{see also|Machine learning terms}} ==Dimension Reduction in Machine Learning== Dimension reduction, also known as dimensionality reduction, is a fundamental technique in the field of machine learning and data analysis. The primary goal of dimension reduction is to reduce the number of features or variables in a dataset while preserving its underlying structure and information. This process aids in improving computational efficiency, reducing the risk of overfitt...")
19:16, 19 March 2023 Walle talk contribs created page Device (Created page with "{{see also|Machine learning terms}} ==Device in Machine Learning== The term "device" in the context of machine learning generally refers to the hardware that is utilized for running machine learning algorithms, models, and training processes. Devices can range from basic personal computers to powerful, specialized processors designed specifically for machine learning tasks. In this article, we will explore the various types of devices used in machine learning, their char...")
19:16, 19 March 2023 Walle talk contribs created page Derived label (Created page with "{{see also|Machine learning terms}} ==Derived Label in Machine Learning== In machine learning, a '''derived label''' refers to the output variable that has been transformed or computed from the raw data in order to improve the performance or interpretability of a model. The process of creating derived labels often involves feature engineering and domain expertise to determine the most relevant or meaningful representations of the data. ===Feature Engineering and Derived...")
19:15, 19 March 2023 Walle talk contribs created page Dense layer (Created page with "{{see also|Machine learning terms}} ==Dense Layer in Machine Learning== A '''dense layer''' in machine learning, also referred to as a '''fully connected layer''' or simply '''FC layer''', is a fundamental architectural component of artificial neural networks (ANNs) and deep learning models. The dense layer functions as a linear transformation followed by an optional non-linear activation function, which facilitates the learning and representation of complex...")
19:15, 19 March 2023 Walle talk contribs created page Demographic parity (Created page with "{{see also|Machine learning terms}} ==Demographic Parity in Machine Learning== Demographic parity, also known as statistical parity, is a fairness metric used in machine learning to assess the performance of classification algorithms with respect to different demographic groups. It measures the extent to which an algorithm's predictions are unbiased with respect to a protected attribute, such as gender, race, or age. The goal of demographic parity is to ensure equal trea...")
19:15, 19 March 2023 Walle talk contribs created page Deep neural network (Created page with "{{see also|Machine learning terms}} ==Introduction== A '''deep neural network''' (DNN) is a type of artificial neural network (ANN) used in machine learning and deep learning that consists of multiple interconnected layers of artificial neurons. DNNs have gained significant attention in recent years due to their ability to effectively model complex and large-scale data, leading to breakthroughs in various domains, such as computer vision, natural langua...")
19:15, 19 March 2023 Walle talk contribs created page Decision threshold (Created page with "{{see also|Machine learning terms}} ==Definition== A '''decision threshold''' is a predefined value or cut-off point that determines the classification of instances in a machine learning algorithm. It is particularly useful in binary classification problems, where a model outputs a probability score for a given instance belonging to one of two classes (e.g., positive or negative). By comparing the probability score to the decision threshold, the model can assign the...")
19:15, 19 March 2023 Walle talk contribs created page Decision boundary (Created page with "{{see also|Machine learning terms}} ==Decision Boundary in Machine Learning== ===Definition=== In machine learning, a '''decision boundary''' is the surface that separates different classes or categories in a classification problem. It represents the boundary in the feature space where the algorithm makes decisions to classify input data points into their respective categories, based on the chosen classification model. A well-defined decision boundary can aid in accurate...")
19:15, 19 March 2023 Walle talk contribs created page Data parallelism (Created page with "{{see also|Machine learning terms}} ==Introduction== Data parallelism is a technique in machine learning that involves the simultaneous processing of data subsets across multiple computational resources to expedite training processes. It is particularly useful when dealing with large-scale datasets and computationally-intensive models, such as deep neural networks and other complex machine learning architectures. By distributing the workload across multiple resou...")
19:15, 19 March 2023 Walle talk contribs created page Data analysis (Created page with "{{see also|Machine learning terms}} ==Introduction== Data analysis in machine learning is the process of inspecting, cleaning, transforming, and modeling data to extract useful information, draw conclusions, and support decision-making. Machine learning is a subfield of artificial intelligence that focuses on designing algorithms and models that can learn from data to make predictions or decisions. In this context, data analysis is crucial in selecting appropriate fe...")
19:14, 19 March 2023 Walle talk contribs created page Cross-validation (Created page with "{{see also|Machine learning terms}} ==Cross-validation in Machine Learning== Cross-validation is a widely used technique in machine learning for estimating the performance of a predictive model. It aims to assess how well a model can generalize to an independent dataset by evaluating its performance on multiple subsets of the training data. This approach helps to mitigate overfitting, a common issue in machine learning where the model learns the training data too wel...")
19:14, 19 March 2023 Walle talk contribs created page Cross-entropy (Created page with "{{see also|Machine learning terms}} ==Introduction== Cross-entropy is a measure of the dissimilarity between two probability distributions, commonly used in machine learning, particularly in the context of training neural networks and other classification models. It serves as a widely used loss function in optimization algorithms, where the objective is to minimize the discrepancy between the predicted distribution and the true distribution of data. In this article,...")
19:14, 19 March 2023 Walle talk contribs created page Coverage bias (Created page with "{{see also|Machine learning terms}} ==Coverage Bias in Machine Learning== Coverage bias, also referred to as sampling bias, is a form of bias that occurs in machine learning when the data used to train a model does not accurately represent the target population or the problem space. This leads to models that may perform well on the training data, but poorly on the general population, ultimately resulting in biased predictions or decisions. The primary cause of coverage b...")
19:14, 19 March 2023 Walle talk contribs created page Counterfactual fairness (Created page with "{{see also|Machine learning terms}} ==Introduction== Counterfactual fairness is a concept in machine learning that aims to ensure that an algorithm's predictions are fair by considering hypothetical alternative outcomes under different conditions. The idea is to create models that make unbiased decisions by accounting for potential biases in data, which could lead to unfair treatment of individuals or groups. This concept is particularly important in the context of sensi...")
19:14, 19 March 2023 Walle talk contribs created page Cost (Created page with "{{see also|Machine learning terms}} ==Definition of Cost in Machine Learning== In the context of machine learning, the term '''cost''' refers to a metric that quantifies the difference between the predicted values generated by a model and the true values of the target variable. This metric, also known as the '''loss function''' or '''objective function''', is an essential component of the optimization process, as it guides the model's learning process to minimize the...")
19:14, 19 March 2023 Walle talk contribs created page Convex set (Created page with "{{see also|Machine learning terms}} ==Definition== In the context of machine learning, a '''convex set''' is a collection of points in a Euclidean space, such that for any two points within the set, the entire line segment connecting these points also lies within the set. Convex sets are fundamental to the study of optimization problems and are particularly important in machine learning due to their desirable properties, which often lead to efficient and robust a...")
19:13, 19 March 2023 Walle talk contribs created page Co-training (Created page with "{{see also|Machine learning terms}} ==Co-training in Machine Learning== Co-training is a semi-supervised learning technique in the domain of machine learning. It leverages both labeled and unlabeled data to improve the performance of classifiers. The technique was first introduced by Avrim Blum and Tom Mitchell in their 1998 paper, ''Combining Labeled and Unlabeled Data with Co-Training''. Co-training is particularly useful when labeled data is scarce, as it make...")
19:13, 19 March 2023 Walle talk contribs created page Dataset API (tf.data) (Created page with "{{see also|Machine learning terms}} ==Introduction== The '''Dataset API (tf.data)''' is a versatile and high-performance input pipeline system designed for use with the TensorFlow machine learning framework. It facilitates the process of loading, preprocessing, and transforming data efficiently, thus allowing for optimal utilization of computational resources during model training and evaluation. The tf.data API is specifically tailored to address the requirements of...")
15:46, 19 March 2023 Walle talk contribs created page Time series analysis (Created page with "{{see also|Machine learning terms}} ==Introduction== Time series analysis is a statistical technique used to identify and analyze patterns and trends in data collected over time. It plays a critical role in various fields, including finance, economics, and meteorology. In machine learning, time series analysis is used to build predictive models that forecast future events based on historical data. The primary goal of time series analysis in machine learning is to extract...")
15:46, 19 March 2023 Walle talk contribs created page Sketching (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, ''sketching'' refers to a technique used to reduce the dimensionality of data, while approximately preserving its essential properties. The primary goal of sketching is to facilitate the efficient processing and analysis of large datasets, which is crucial for the success of various machine learning algorithms. This article provides an overview of sketching techniques, their applic...")
15:46, 19 March 2023 Walle talk contribs created page Similarity measure (Created page with "{{see also|Machine learning terms}} ==Similarity Measure in Machine Learning== A '''similarity measure''' is a metric used in machine learning to quantify the degree of resemblance between two objects or data points. Similarity measures are essential for many machine learning tasks, such as clustering, classification, and recommender systems. These metrics facilitate the identification of similar instances and the organization of data into meaningful grou...")
15:46, 19 March 2023 Walle talk contribs created page K-median (Created page with "{{see also|Machine learning terms}} ==Introduction== The '''k-median''' algorithm is a popular unsupervised learning technique in the field of machine learning and data science. It is a variant of the well-known k-means clustering algorithm, which aims to partition a set of data points into ''k'' distinct clusters, where each data point belongs to the cluster with the nearest mean. The k-median algorithm, on the other hand, seeks to minimize the sum of distan...")
15:46, 19 March 2023 Walle talk contribs created page K-means (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning and data analysis, '''k-means''' is an unsupervised clustering algorithm that partitions a dataset into '''k''' distinct clusters. The algorithm aims to minimize the sum of squared distances between the data points and the centroids of their corresponding clusters. It is widely used for a variety of applications such as pattern recognition, image segmentation, and customer segmentation....")
15:46, 19 March 2023 Walle talk contribs created page Convex optimization (Created page with "{{see also|Machine learning terms}} ==Introduction== Convex optimization is a subfield of mathematical optimization that deals with the minimization (or maximization) of convex functions over convex sets. In the context of machine learning, convex optimization plays a crucial role in finding the best model parameters, given a particular training dataset and a loss function. This field has gained significant attention in recent years, as it provides reliable and efficient...")
15:45, 19 March 2023 Walle talk contribs created page Convex function (Created page with "{{see also|Machine learning terms}} ==Definition== A '''convex function''' is a type of function that has particular mathematical properties, which are especially useful in the field of machine learning. Formally, a function ''f'' : ''R^n'' → ''R'' is called convex if, for all points ''x'' and ''y'' in its domain and for any scalar ''t'' in the range of 0 ≤ ''t'' ≤ 1, the following inequality holds: f(tx + (1 - t)y) ≤ tf(x) + (1 - t)f(y) This property ensur...")
15:45, 19 March 2023 Walle talk contribs created page Convenience sampling (Created page with "{{see also|Machine learning terms}} ==Introduction== Convenience sampling, also known as opportunity sampling or accidental sampling, is a non-probability sampling method utilized in various fields, including machine learning and statistics. It involves selecting a sample based on its accessibility and ease of collection, rather than following a random sampling process. Despite its limitations, convenience sampling can serve as a useful preliminary step for exploratory r...")
15:45, 19 March 2023 Walle talk contribs created page Confirmation bias (Created page with "{{see also|Machine learning terms}} ==Definition== Confirmation bias in machine learning refers to the phenomenon where a learning algorithm tends to prioritize or overfit data that confirms its pre-existing beliefs or hypotheses, while ignoring or underfitting data that contradicts them. This type of bias may arise from various sources, such as biased training data, biased model initialization, or biased model architectures. The existence of confirmation bias in machine...")
15:45, 19 March 2023 Walle talk contribs created page Collaborative filtering (Created page with "{{see also|Machine learning terms}} ==Introduction== Collaborative filtering (CF) is a widely-used technique in the field of machine learning, specifically in the domain of recommendation systems. It leverages the behavior or preferences of users within a community to make personalized recommendations for individual users. Collaborative filtering can be broadly categorized into two main approaches: user-based and item-based collaborative filtering. ==User-based Collabor...")
15:45, 19 March 2023 Walle talk contribs created page Co-adaptation (Created page with "{{see also|Machine learning terms}} ==Co-adaptation in Machine Learning== Co-adaptation is a phenomenon in machine learning that occurs when a model becomes too reliant on certain features or training examples, leading to a decrease in generalization performance. This article provides an overview of co-adaptation in the context of machine learning, its implications, and methods for mitigating its effects. ===Definition and Causes=== In machine learning, co-adaptation re...")
15:45, 19 March 2023 Walle talk contribs created page Checkpoint (Created page with "{{see also|Machine learning terms}} ==Definition== In machine learning, a '''checkpoint''' refers to a snapshot of the current state of a model during the training process. Checkpoints are primarily used for saving the model's weights and architecture, and sometimes additional information such as learning rates and optimizer states, at regular intervals or after a specified number of iterations. This allows the training process to be resumed from a previous state in...")
15:44, 19 March 2023 Walle talk contribs created page Candidate sampling (Created page with "{{see also|Machine learning terms}} ==Candidate Sampling in Machine Learning== Candidate sampling is a method used in machine learning, particularly in the context of training large-scale models. It is an optimization technique that reduces the computational complexity of learning algorithms by approximating the gradient of the loss function. In this section, we will explore the concept of candidate sampling, its motivation, and its applications in machine learning. ===...")
15:44, 19 March 2023 Walle talk contribs created page Candidate generation (Created page with "{{see also|Machine learning terms}} ==Candidate Generation in Machine Learning== Candidate generation is a critical process in machine learning (ML) that involves identifying a set of potential solutions, or "candidates," to solve a specific problem. This process is commonly used in various ML tasks, such as recommender systems, pattern mining, and search algorithms. The main goal of candidate generation is to efficiently explore the solution space and reduce...")