18 March 2023
- 19:0219:02, 18 March 2023 diff hist +2,571 N 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..." current
- 19:0219:02, 18 March 2023 diff hist +4,051 N 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..." current
- 19:0219:02, 18 March 2023 diff hist +3,036 N 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..." current
- 19:0119:01, 18 March 2023 diff hist +3,697 N 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..." current
- 19:0119:01, 18 March 2023 diff hist +4,307 N 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..." current
- 19:0119:01, 18 March 2023 diff hist +3,940 N 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..." current
- 19:0119:01, 18 March 2023 diff hist +3,425 N 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..." current
- 19:0119:01, 18 March 2023 diff hist +3,720 N 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..." current
- 19:0119:01, 18 March 2023 diff hist +3,352 N Axis-aligned condition Created page with "{{see also|Machine learning terms}} ==Axis-Aligned Condition in Machine Learning== The axis-aligned condition is a concept commonly used in various machine learning algorithms, especially in the context of decision trees and spatial data structures. This condition refers to a restriction imposed on the decision boundaries, such that they are parallel to the coordinate axes of the feature space. The concept is relevant for understanding the behavior, limitations, and impr..." current
- 19:0019:00, 18 March 2023 diff hist +4,134 N Transformer Created page with "{{see also|Machine learning terms}} ==Introduction== In machine learning, the '''Transformer''' is a deep learning architecture that has revolutionized the field of natural language processing (NLP) since its introduction in 2017 by Vaswani et al. in the paper "Attention is All You Need" 1. The Transformer model leverages self-attention mechanisms to effectively capture long-range dependencies and contextual information in sequence data. It has been the foundation fo..." current
- 13:3013:30, 18 March 2023 diff hist +2,809 N Synthetic feature Created page with "{{see also|Machine learning terms}} ==Synthetic Feature in Machine Learning== In the domain of machine learning and data science, a synthetic feature, also known as a feature engineering or constructed feature, refers to a new attribute or variable that is generated through the transformation or combination of existing features. This process aims to improve the performance and interpretability of machine learning models by providing additional, relevant informati..." current
- 13:2913:29, 18 March 2023 diff hist +3,697 N Supervised machine learning Created page with "{{see also|Machine learning terms}} ==Introduction== Supervised machine learning is an approach in the field of machine learning where a model is trained using labeled data, which consists of input-output pairs. This type of learning aims to establish a relationship between input features and corresponding target outputs, allowing the model to make predictions on new, previously unseen data. Supervised learning is widely used in various applications, including imag..." current
- 13:2913:29, 18 March 2023 diff hist +3,672 N Stochastic gradient descent (SGD) Created page with "{{see also|Machine learning terms}} ==Introduction== '''Stochastic gradient descent''' ('''SGD''') is an optimization algorithm commonly used in machine learning and deep learning to minimize a given objective function. It is a variant of the gradient descent algorithm that performs updates on a randomly selected subset of the data, rather than the entire dataset, at each iteration. This approach offers several advantages, including faster convergence and the abi..." current
- 13:2913:29, 18 March 2023 diff hist +3,999 N Stationarity Created page with "{{see also|Machine learning terms}} ==Introduction== In machine learning, '''stationarity''' refers to a property of time series data or stochastic processes where the statistical properties, such as the mean and variance, remain constant over time. It is an important concept in various machine learning tasks, particularly in time series analysis and forecasting, as it enables the development of reliable models and the identification of patterns and trends in..." current
- 13:2913:29, 18 March 2023 diff hist +2,989 N Static inference Created page with "{{see also|Machine learning terms}} ==Introduction== Static inference is a technique in machine learning that involves predicting the output of a given input without explicitly training a model on the input data. It is a form of inference that relies on a model's prior knowledge and pre-existing learned representations, rather than adjusting its parameters to fit the data at hand. This approach is particularly useful in situations where the data is sparse, noisy, or..." current
- 13:2913:29, 18 March 2023 diff hist +2,850 N Static Created page with "{{see also|Machine learning terms}} ==Static in Machine Learning== Static in machine learning refers to the invariant aspects or fixed properties of a learning model or dataset. These properties remain unchanged throughout the model's learning process and its subsequent deployment. This contrasts with dynamic aspects, which can be altered or adapted as the model evolves. Static properties are crucial for establishing a baseline and ensuring consistent performance of a ma..." current
- 13:2913:29, 18 March 2023 diff hist +3,648 N Staged training Created page with "{{see also|Machine learning terms}} ==Introduction== Staged training is a technique in machine learning that involves training a model in successive stages, each with a distinct objective, in order to improve overall performance. This method is particularly useful for training deep learning models, as it helps to overcome challenges such as vanishing gradients, optimization difficulties, and training instability. Staged training can be applied to a variety of domains, in..." current
- 13:2913:29, 18 March 2023 diff hist +2,824 N Squared loss Created page with "{{see also|Machine learning terms}} ==Squared Loss== Squared loss, also known as mean squared error (MSE) or L2 loss, is a widely used loss function in machine learning and statistical modeling for measuring the discrepancy between predicted values and true values in a given dataset. The objective of any machine learning model is to minimize the loss function, which in turn improves the model's prediction accuracy. ===Definition=== Formally, the squared loss..." current
- 13:2813:28, 18 March 2023 diff hist +3,364 N Sparse vector Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, a '''sparse vector''' is a vector representation of data that contains a significant number of zero-valued elements. Sparse vectors are widely used in various applications, such as natural language processing, information retrieval, and recommender systems, to name a few. This article will discuss the concept of sparse vectors, their properties, and applications in machine learning. =..." current
- 13:2813:28, 18 March 2023 diff hist +4,341 N Sparse representation Created page with "{{see also|Machine learning terms}} ==Sparse Representation in Machine Learning== Sparse representation is a concept in machine learning and signal processing that involves encoding data or signals using a small number of non-zero coefficients. This approach has become popular due to its ability to capture the essential features of the data, while reducing the computational complexity and storage requirements. Sparse representations have been successfully applied in vari..." current
- 13:2813:28, 18 March 2023 diff hist +3,631 N Sparse feature Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, a sparse feature is a representation of data that consists predominantly of zero or null values, indicating the absence of some attributes or characteristics. Sparse features can be found in various data types and domains, such as text data, image data, and graph data. Utilizing sparse features effectively can significantly improve the efficiency and performance of machine learning alg..." current
- 13:2813:28, 18 March 2023 diff hist +3,252 N Softmax Created page with "{{see also|Machine learning terms}} ==Introduction== In machine learning, the '''softmax function''' is a widely used mathematical function for transforming a vector of numerical values into a probability distribution. Softmax is particularly useful in classification tasks where the goal is to assign an input to one of several possible categories. Softmax is often employed in combination with neural networks, such as multilayer perceptrons and convolutional neu..." current
- 13:2813:28, 18 March 2023 diff hist +2,942 N Sigmoid function Created page with "{{see also|Machine learning terms}} ==Introduction== In machine learning, the '''sigmoid function''' is a widely used mathematical function that transforms input values into probabilities, ranging from 0 to 1. It is often employed in various types of machine learning algorithms, particularly in artificial neural networks and logistic regression models, to map continuous inputs to probabilities for binary classification tasks. The sigmoid function is characterized..." current
- 13:2813:28, 18 March 2023 diff hist +3,189 N Sequence-to-sequence task Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, particularly deep learning, a '''sequence-to-sequence (seq2seq) task''' refers to the process of mapping an input sequence to an output sequence. This type of task is particularly useful in various natural language processing (NLP) and time series prediction applications. It has gained significant attention in recent years due to the advancements in recurrent neural networks (RNNs)..." current
- 13:2713:27, 18 March 2023 diff hist +3,622 N Sentiment analysis Created page with "{{see also|Machine learning terms}} ==Introduction== Sentiment analysis, also known as opinion mining or emotion AI, is a subfield of Natural Language Processing (NLP) in machine learning that focuses on determining the sentiment, emotions, or opinions expressed in a given text. It is commonly applied to a wide range of areas, such as social media monitoring, customer feedback analysis, and market research. ==Approaches to Sentiment Analysis== There are three pr..." current
- 13:2713:27, 18 March 2023 diff hist +3,686 N Self-attention (also called self-attention layer) Created page with "{{see also|Machine learning terms}} ==Introduction== Self-attention, also known as the self-attention layer, is a mechanism used in machine learning models, particularly in deep learning architectures such as Transformers. It enables the models to weigh and prioritize different input elements based on their relationships and relevance to one another. Self-attention has been widely adopted in various applications, including nat..." current
- 13:2713:27, 18 March 2023 diff hist +2,995 N Regularization rate Created page with "{{see also|Machine learning terms}} ==Regularization Rate in Machine Learning== Regularization is an important technique in machine learning that helps prevent overfitting, a common problem where a model performs well on the training data but does not generalize well to new, unseen data. The regularization rate, also known as the regularization parameter or hyperparameter, is a constant value used to control the strength of regularization applied to a learning algorithm...." current
- 13:2713:27, 18 March 2023 diff hist +3,000 N Regularization Created page with "{{see also|Machine learning terms}} ==Regularization in Machine Learning== Regularization is a technique used in machine learning to prevent overfitting, which occurs when a model learns to perform well on the training data but does not generalize well to unseen data. Regularization works by adding a penalty term to the objective function, which encourages the model to select simpler solutions that are more likely to generalize to new data. There are several types of reg..." current
- 13:2713:27, 18 March 2023 diff hist +3,623 N Regression model Created page with "{{see also|Machine learning terms}} ==Introduction== A regression model in machine learning is a type of supervised learning algorithm that is designed to predict continuous output values, based on input features. The main goal of regression models is to understand the relationships between the dependent variable (target) and the independent variables (features). Regression models have been widely adopted in various fields such as finance, healthcare, and economics,..." current
- 13:2713:27, 18 March 2023 diff hist +3,212 N Rater Created page with "{{see also|Machine learning terms}} ==Rater in Machine Learning== In the field of machine learning, a '''rater''' refers to an individual or group responsible for evaluating and scoring a model's predictions, usually by comparing them to a known ground truth. Raters play a crucial role in the development, training, and validation of machine learning algorithms, ensuring that models are accurate, reliable, and unbiased. ===Role of Raters in Machine Learning=== Raters are..." current
- 13:2613:26, 18 March 2023 diff hist +2,488 N Proxy labels Created page with "{{see also|Machine learning terms}} ==Proxy Labels in Machine Learning== Proxy labels are a technique used in the field of machine learning to approximate the true labels of a dataset when obtaining the exact labels is infeasible or expensive. This method is particularly useful in situations where acquiring ground truth labels would require a significant investment of time or resources, or when the true labels are not directly observable. ===Applications=== Proxy la..." current
- 13:2613:26, 18 March 2023 diff hist +3,623 N Prediction Created page with "{{see also|Machine learning terms}} ==Introduction== Prediction in machine learning refers to the process by which a trained model estimates or forecasts the outcome of a given input based on its learned patterns and relationships from past data. The prediction task is essential to various machine learning applications, including classification, regression, and time series forecasting. This article provides an overview of the concept of prediction in machine..." current
- 13:2613:26, 18 March 2023 diff hist +3,809 N Post-processing Created page with "{{see also|Machine learning terms}} ==Introduction== Post-processing, in the context of machine learning, refers to a set of techniques and methods applied to the output of a machine learning model in order to improve or refine its results. This may include steps such as data transformation, calibration, and thresholding. Post-processing is often used to enhance model performance, interpretability, and reliability when deployed in real-world applications. ==Purpose of P..." current
- 13:2613:26, 18 March 2023 diff hist +3,372 N Positive class Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, the term '''positive class''' refers to one of the two possible outcomes in a binary classification problem. Binary classification is a type of supervised learning where the objective is to categorize a given input into one of two mutually exclusive categories or classes. These classes are often labeled as the positive and negative classes. The positive class is typically the t..." current
- 13:2613:26, 18 March 2023 diff hist +3,470 N Pipelining Created page with "{{see also|Machine learning terms}} ==Pipelining in Machine Learning== Pipelining in machine learning refers to the process of chaining together multiple steps of a machine learning workflow, from data preprocessing and feature extraction to model training and evaluation, to create an efficient and organized end-to-end solution. Pipelining is commonly used to simplify the implementation, facilitate the management, and improve the reproducibility of complex machine learni..." current
- 13:2613:26, 18 March 2023 diff hist +3,448 N Parameter Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, a '''parameter''' refers to a variable that is adjusted during the model training process to minimize errors and improve the accuracy of the model's predictions. These parameters enable the model to learn from data and represent the relationship between input features and target outputs. This article will provide an overview of parameters in machine learning, including their role in th..." current
- 13:2613:26, 18 March 2023 diff hist +3,017 N Pandas Created page with "{{see also|Machine learning terms}} ==Introduction== Pandas is a widely used, open-source data manipulation and analysis library in Python that provides flexible, high-performance data structures for efficient handling of large and complex datasets. Although not specifically designed for machine learning, it has become an essential tool for data preprocessing, cleaning, and transformation tasks in the Machine Learning pipeline. ==Fe..." current
- 13:2513:25, 18 March 2023 diff hist +3,570 N Overfitting Created page with "{{see also|Machine learning terms}} ==Overfitting in Machine Learning== ===Definition=== Overfitting is a phenomenon that occurs in machine learning when a model becomes excessively tailored to the training dataset, resulting in a decrease in its generalization performance on unseen data. In essence, the model learns the noise and peculiarities present in the training data, which negatively impacts its ability to make accurate predictions for new, unseen data. Overfittin..." current
- 13:2513:25, 18 March 2023 diff hist +2,626 N Output layer Created page with "{{see also|Machine learning terms}} ==Introduction== In the context of machine learning and artificial neural networks, the '''output layer''' is a crucial component that translates the computational results from the hidden layers into meaningful and interpretable predictions or classifications. The output layer, consisting of one or more neurons, is responsible for generating the final output of a neural network model, which can be used for various purposes,..." current
- 13:2513:25, 18 March 2023 diff hist +3,667 N Online inference Created page with "{{see also|Machine learning terms}} ==Online Inference in Machine Learning== ===Overview=== Online inference in machine learning refers to the process of making predictions or drawing conclusions in real-time based on new data, as opposed to relying on a pre-trained model. This approach is commonly employed in situations where data is received incrementally and predictions must be made promptly, such as in recommendation systems, financial markets, or real-time computer..." current
- 13:2513:25, 18 March 2023 diff hist +3,139 N One-vs.-all Created page with "{{see also|Machine learning terms}} ==One-vs.-All in Machine Learning== One-vs.-all (OvA), also known as one-vs.-rest (OvR) or one-against-all, is a multi-class classification strategy commonly used in machine learning. It is a method for training a classifier to distinguish between multiple classes by converting the multi-class problem into several binary classification problems. The key idea is to train a separate binary classifier for each class, treating it as the po..." current
- 13:2513:25, 18 March 2023 diff hist +3,145 N One-hot encoding Created page with "{{see also|Machine learning terms}} ==One-Hot Encoding== One-hot encoding is a widely used technique in the field of machine learning and data preprocessing. It is employed to convert categorical variables into a numerical format that is suitable for machine learning algorithms to process. This method involves transforming a categorical variable into a binary vector, where each category is represented by a unique combination of zeros and ones. ===Background=== C..." current
- 13:2513:25, 18 March 2023 diff hist +2,660 N Offline inference Created page with "{{see also|Machine learning terms}} ==Offline Inference in Machine Learning== Offline inference, also known as batch inference, is a process in machine learning whereby a trained model is used to make predictions on a dataset in a non-interactive or non-real-time manner. This approach allows for the efficient processing of large datasets, as it does not require an immediate response to user inputs. ===Characteristics of Offline Inference=== Offline inference is char..."
- 13:2413:24, 18 March 2023 diff hist +3,269 N Offline Created page with "{{see also|Machine learning terms}} ==Offline Machine Learning== Offline machine learning, also known as batch learning or learning from static data, is a type of machine learning methodology where an algorithm is trained on a fixed dataset before deployment, rather than continuously updating its knowledge based on new data. In this approach, the model's training and testing phases are separate, and the model's generalization capabilities are of utmost importance..." current
- 13:2413:24, 18 March 2023 diff hist +3,890 N Numerical data Created page with "{{see also|Machine learning terms}} ==Introduction== Numerical data, also referred to as quantitative data, is a type of data used extensively in machine learning and other computational disciplines. It consists of data points that can be represented and manipulated using numbers. In machine learning, numerical data is particularly important as it forms the foundation for mathematical models and algorithms that learn patterns and make predictions. This article will d..." current
- 13:2413:24, 18 March 2023 diff hist +3,052 N Normalization Created page with "{{see also|Machine learning terms}} ==Normalization in Machine Learning== Normalization is a crucial preprocessing step in machine learning that aims to scale features or data points to a standardized range or distribution. By transforming the data to a common scale, it helps machine learning algorithms converge faster and achieve better performance. This is particularly important for algorithms that are sensitive to the scale of input features, such as gradient descen..." current
- 13:2413:24, 18 March 2023 diff hist +4,182 N Nonstationarity Created page with "{{see also|Machine learning terms}} ==Introduction== Nonstationarity is a significant concept in the field of machine learning and statistics, which refers to the phenomenon where the underlying properties of a data-generating process change over time. In many real-world problems, the data encountered by machine learning models are subject to such changes, making it challenging to develop algorithms that can adapt and maintain their performance. In this article,..." current
- 13:2413:24, 18 March 2023 diff hist +3,334 N Nonlinear Created page with "{{see also|Machine learning terms}} ==Introduction== Nonlinear methods in machine learning refer to a class of algorithms and techniques that are designed to model complex relationships between input and output variables, which cannot be adequately captured by linear models. These nonlinear models are particularly useful in situations where the underlying relationships between variables are more intricate or involve higher-order interactions. In this article, we will dis..." current
- 13:2413:24, 18 March 2023 diff hist +3,274 N Node (neural network) Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, a node, also known as a neuron or unit, is a fundamental component of a neural network. It is responsible for receiving, processing, and transmitting information within the network. The functioning of nodes is inspired by the biological neurons found in the human brain, although the two differ significantly in their complexity and operation. ==Structure and Function== ===Input..." current
- 13:2413:24, 18 March 2023 diff hist +3,462 N Neuron Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, a '''neuron''' refers to an elementary unit within an artificial neural network (ANN). These units, also known as nodes or artificial neurons, are inspired by biological neurons found in the nervous systems of living organisms. Neurons in ANNs serve to process and transmit information through the network, enabling various machine learning tasks such as classification, regression, and p..." current
- 13:2313:23, 18 March 2023 diff hist +3,011 N Negative class Created page with "{{see also|Machine learning terms}} ==Negative Class in Machine Learning== The negative class in machine learning refers to the category or label assigned to instances in a dataset that do not possess the characteristics or features of interest. It is the counterpart to the positive class, which represents instances with the desired attributes. The concept of negative and positive classes is particularly relevant in binary classification problems, where the goal..." current
- 13:2313:23, 18 March 2023 diff hist +4,198 N Natural language understanding Created page with "{{see also|Machine learning terms}} ==Introduction== Natural Language Understanding (NLU) is a subfield of Artificial Intelligence and Computational Linguistics, concerned with enabling machines to comprehend, interpret, and generate human language in a meaningful way. NLU plays a pivotal role in the development of Machine Learning models, which are designed to automatically learn and improve from experience, with a focus on tasks such as Sentiment Analysis..." current
- 13:2313:23, 18 March 2023 diff hist +4,018 N Multimodal model Created page with "{{see also|Machine learning terms}} ==Introduction== A '''multimodal model''' in machine learning is an advanced computational approach that involves the integration and processing of multiple types of data, or modalities, to enhance the learning process and improve predictive performance. Multimodal models aim to capture complex patterns and relationships that exist within and across various data modalities, such as text, images, audio, and video, to enable more acc..." current
- 13:2313:23, 18 March 2023 diff hist +3,303 N Multi-head self-attention Created page with "{{see also|Machine learning terms}} ==Introduction== Multi-head self-attention is a core component of the Transformer architecture, a type of neural network introduced by Vaswani et al. (2017) in the paper "Attention Is All You Need". This mechanism allows the model to capture complex relationships between the input tokens by weighing their importance with respect to each other. The multi-head aspect refers to the parallel attention computations performed on differen..." current
- 13:2313:23, 18 March 2023 diff hist +4,009 N Multi-class classification Created page with "{{see also|Machine learning terms}} ==Introduction== Multi-class classification is a type of supervised learning problem in machine learning where an algorithm is tasked with categorizing instances into one of multiple possible classes. In contrast to binary classification, which deals with only two classes, multi-class classification handles three or more classes. This article provides an overview of multi-class classification, discusses common techniques an..." current
- 13:2313:23, 18 March 2023 diff hist +4,375 N Model parallelism Created page with "{{see also|Machine learning terms}} ==Model Parallelism in Machine Learning== Model parallelism is an approach in machine learning that addresses the computational challenges posed by the increasing size and complexity of modern neural network models. It involves the concurrent execution of different parts of a single model across multiple processing units, often in parallel to other parts of the model. This article discusses the motivation behind model parallelism,..." current
- 13:2213:22, 18 March 2023 diff hist +3,122 N Model Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, a '''model''' refers to a mathematical representation or abstraction of a real-world process or phenomenon. Machine learning models are developed using algorithms that learn from and make predictions or decisions based on input data. The primary goal of these models is to generalize from the training data in order to accurately predict outcomes for unseen data points. ==Types of M..." current
- 13:2213:22, 18 March 2023 diff hist +3,995 N Modality Created page with "{{see also|Machine learning terms}} ==Introduction== In the context of machine learning, '''modality''' refers to the different types, forms, or structures of data that a model can process or learn from. Understanding the concept of modality is essential for designing and implementing machine learning algorithms that can handle diverse data types effectively. This article discusses the concept of modality in machine learning, its various types, and its importance in mode..." current
- 13:2113:21, 18 March 2023 diff hist +4,259 N Meta-learning Created page with "{{see also|Machine learning terms}} ==Introduction== Meta-learning, also referred to as "learning to learn", is an advanced paradigm in the field of machine learning that focuses on the design of algorithms and models capable of improving their performance on new tasks by utilizing previous learning experiences. The primary objective of meta-learning is to develop models that can adapt quickly to new tasks with minimal data and training time. This article provides an..." current
- 13:2113:21, 18 March 2023 diff hist +2,712 N Masked language model Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, particularly natural language processing (NLP), a '''masked language model''' (MLM) is an important and widely used approach to train deep learning models on large-scale text data. This unsupervised technique has gained significant attention due to its success in various NLP tasks, such as text classification, translation, and sentiment analysis. ==Masked Language Modeling== Masked la..." current
- 13:1913:19, 18 March 2023 diff hist +2,990 N Loss function Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, a '''loss function''' (also known as a cost function or objective function) is a crucial mathematical formulation that quantifies the difference between the predicted outcome of a model and the actual or desired outcome. Loss functions serve as the basis for optimization, enabling the model to iteratively adjust its parameters to minimize this difference and improve its performance..." current
- 13:1913:19, 18 March 2023 diff hist +2,890 N Loss curve Created page with "{{see also|Machine learning terms}} ==Loss Curve in Machine Learning== In the field of machine learning, a '''loss curve''' is a graphical representation that demonstrates the performance of a learning algorithm during its training process. By plotting the value of the loss function against the number of training iterations or epochs, researchers and practitioners can assess the algorithm's progress in learning the underlying patterns in the given dataset. ===Im..." current
- 13:1913:19, 18 March 2023 diff hist +3,549 N Loss Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, ''loss'' refers to a quantitative measure of the discrepancy between a model's predicted outputs and the true or observed values. It serves as an evaluation metric to assess the performance of a machine learning algorithm during the training process. By minimizing the loss function, practitioners aim to improve the model's accuracy and generalization capabilities. ==Loss Functions..." current
- 13:1913:19, 18 March 2023 diff hist +2,793 N Logistic regression Created page with "{{see also|Machine learning terms}} ==Introduction== Logistic regression is a statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome. In the context of machine learning, logistic regression is a supervised learning algorithm used for solving binary classification problems. It predicts the probability of an event occurring based on the given input features. ==Logistic Regression Model== ===Model F..." current
- 13:1913:19, 18 March 2023 diff hist +3,328 N Log-odds Created page with "{{see also|Machine learning terms}} ==Log-odds in Machine Learning== Log-odds, also known as logit, is a concept frequently used in machine learning, particularly in the context of binary classification problems. It is a method of representing the probability of an event occurring in the form of a logarithmic function. The log-odds function is often used in conjunction with logistic regression, a popular machine learning algorithm for classification tasks. ===Definition..." current
- 13:1913:19, 18 March 2023 diff hist +2,863 N Linear regression Created page with "{{see also|Machine learning terms}} ==Linear Regression in Machine Learning== Linear regression is a fundamental supervised learning technique used in machine learning and statistics to model the relationship between a dependent variable and one or more independent variables. It is a linear approach that assumes a linear relationship between input and output variables. ===Overview=== In machine learning, linear regression is a popular algorithm for solving reg..." current
- 13:1913:19, 18 March 2023 diff hist +3,597 N Linear model Created page with "{{see also|Machine learning terms}} ==Linear Models in Machine Learning== Linear models are a class of statistical models and machine learning algorithms that assume a linear relationship between input features and output. They are often used for regression and classification tasks due to their simplicity and ease of interpretation. ===Introduction=== In machine learning, linear models are used to predict a target variable based on one or more input features. These..." current
- 13:1813:18, 18 March 2023 diff hist +3,518 N Linear Created page with "{{see also|Machine learning terms}} ==Linear Models in Machine Learning== ===Introduction=== In machine learning, linear models are a class of algorithms that utilize a linear relationship between input features and the output variable to make predictions. These models assume that the relationship between the input features (independent variables) and the output (dependent variable) can be represented by a straight line, or more generally, a hyperplane in higher-..." current
- 13:1613:16, 18 March 2023 diff hist +3,821 N Large language model Created page with "{{see also|Machine learning terms}} ==Introduction== A large language model in machine learning refers to an advanced type of artificial intelligence that is designed to understand and generate human-like text. These models are trained on vast amounts of text data and can perform various tasks, such as translation, summarization, and question answering. The development of large language models has been driven by advancements in both deep learning and natural la..." current
- 13:1613:16, 18 March 2023 diff hist +3,427 N Language model Created page with "{{see also|Machine learning terms}} ==Introduction== A '''language model''' in the context of machine learning is a computational model designed to understand and generate human language. Language models leverage statistical and probabilistic techniques to analyze, process, and produce text or speech data, making them indispensable in a wide range of natural language processing (NLP) tasks. Over time, the development of increasingly sophisticated models has led to signif..."
- 13:1513:15, 18 March 2023 diff hist +2,526 N Lambda Created page with "{{see also|Machine learning terms}} ==Lambda in Machine Learning== Lambda is a term commonly used in machine learning and refers to a hyperparameter associated with regularization techniques. It is particularly relevant in the context of linear regression and logistic regression models, where regularization is employed to prevent overfitting and improve the generalization ability of the model. The two most popular regularization techniques using lambda are L1 reg..." current
- 13:1513:15, 18 March 2023 diff hist +3,682 N Labeled example Created page with "{{see also|Machine learning terms}} ==Labeled Example in Machine Learning== ===Definition=== In the field of machine learning, a labeled example refers to a data point that consists of an input feature vector and its corresponding output value, often referred to as the target or label. Labeled examples are essential for supervised learning algorithms, which use these examples to learn a model that can make predictions or classifications on unseen data. The process of..." current
- 13:1513:15, 18 March 2023 diff hist +3,008 N Label Created page with "{{see also|Machine learning terms}} ==Definition== In machine learning, a '''label''' refers to the desired output, or the "correct" value, for a particular instance in a dataset. Labels are used in supervised learning algorithms, where the goal is to learn a mapping from input data to output data, based on a set of examples containing input-output pairs. These output values in the training dataset are known as labels. The process of assigning labels to instances..." current
- 13:1513:15, 18 March 2023 diff hist +3,546 N Encoder Created page with "{{see also|Machine learning terms}} ==Overview== An '''encoder''' in the context of machine learning refers to a specific component of a broader class of algorithms, typically used in unsupervised learning tasks, such as dimensionality reduction and representation learning. Encoders work by transforming input data into a lower-dimensional, more compact representation, which can be efficiently used for further processing, such as for clustering, classificati..." current
- 13:1513:15, 18 March 2023 diff hist +3,376 N Embedding vector Created page with "{{see also|Machine learning terms}} ==Introduction== An '''embedding vector''' in machine learning refers to a continuous, dense representation of discrete objects such as words, images, or nodes in a graph. Embedding vectors are used to convert these discrete objects into a continuous space, which makes it easier to apply machine learning algorithms that rely on mathematical operations. Typically, these embeddings are generated through unsupervised or supervised learnin..." current
- 13:1513:15, 18 March 2023 diff hist +3,971 N Embedding space Created page with "{{see also|Machine learning terms}} ==Introduction== In machine learning, the concept of '''embedding space''' refers to a continuous, high-dimensional space where objects, such as words, images, or user profiles, can be represented as vectors. These vector representations capture the underlying relationships and semantics of the objects in a more compact and computationally efficient manner. Embedding spaces are utilized in various machine learning applications, includi..." current
- 13:1413:14, 18 March 2023 diff hist +4,226 N Denoising Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, denoising refers to the process of removing noise from the input data, which can significantly improve the performance and reliability of the resulting models. Noise in data can arise from various sources, such as measurement errors, transmission errors, or other disturbances. Denoising techniques play a crucial role in many applications, including image processing, speech recognition,..." current
- 13:1413:14, 18 March 2023 diff hist +2,808 N Decoder Created page with "{{see also|Machine learning terms}} ==Decoder in Machine Learning== The '''decoder''' is a fundamental component in various machine learning architectures, particularly in sequence-to-sequence (seq2seq) models and autoencoders. It is responsible for generating output sequences or reconstructing input data based on the internal representation or context vector provided by the encoder. Decoders can be utilized in a wide array of applications such as natural langu..." current
- 13:1413:14, 18 March 2023 diff hist +3,240 N Crash blossom Created page with "{{see also|Machine learning terms}} ==Crash Blossom in Machine Learning== Crash blossom is a term that originates from the field of journalism and linguistic ambiguity, referring to a headline that can be interpreted in more than one way, often resulting in humorous or confusing interpretations. However, in the context of machine learning, crash blossom does not have a direct application or meaning. Nevertheless, we can discuss related concepts in machine learning that t..." current
- 13:1413:14, 18 March 2023 diff hist +3,395 N Confusion matrix Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning and pattern recognition, a '''confusion matrix''', also known as an '''error matrix''' or '''classification matrix''', is a specific table layout that allows for visualization and analysis of the performance of an algorithm, usually a classifier. It is a useful tool to assess the correctness and accuracy of a classification model by comparing the predicted outcomes with the actu..." current
- 13:1413:14, 18 March 2023 diff hist +3,652 N Causal language model Created page with "{{see also|Machine learning terms}} ==Introduction== A '''causal language model''' is a type of machine learning model designed to generate text by predicting the next word in a sequence based on the context of the preceding words. These models are particularly useful in natural language processing (NLP) tasks, as they can capture the inherent structure and meaning of language in a probabilistic manner. Causal language models, which are also known as autoregressive l..." current
- 13:1413:14, 18 March 2023 diff hist +2,718 N Bigram Created page with "{{see also|Machine learning terms}} ==Bigram in Machine Learning== A '''bigram''' is a fundamental concept in the field of natural language processing (NLP), a subfield of machine learning. Bigrams are pairs of consecutive words in a given text or sequence of words. They play a vital role in various NLP tasks, such as language modeling, text classification, and sentiment analysis, by capturing the contextual information of words in a language. ===Definition and..."
- 13:1413:14, 18 March 2023 diff hist +3,105 N Bidirectional language model Created page with "{{see also|Machine learning terms}} ==Bidirectional Language Models in Machine Learning== Bidirectional language models (BiLMs) are a type of machine learning model that are specifically designed for natural language processing (NLP) tasks. They have gained popularity in recent years due to their superior ability to understand and generate human-like text. This article provides an overview of bidirectional language models, their architecture, and applications in NLP task..." current
- 13:1313:13, 18 March 2023 diff hist +3,284 N Bidirectional Created page with "{{see also|Machine learning terms}} ==Bidirectional Approaches in Machine Learning== Bidirectional approaches in machine learning refer to a class of algorithms designed to process and analyze data sequences in both forward and backward directions. These algorithms are particularly useful for tasks involving natural language processing, time series analysis, and other domains where temporal or sequential dependencies exist within the data. In this article, we will discus..." current
- 13:1313:13, 18 March 2023 diff hist +3,334 N Bag of words Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, the '''bag of words''' (BoW) model is a common and simplified representation method used for natural language processing (NLP) and text classification tasks. The primary goal of the BoW model is to convert a collection of text documents into numerical feature vectors, which can be used as input for machine learning algorithms. ==Methodology== The bag of words model comprises two main..." current
- 13:1313:13, 18 March 2023 diff hist +2,635 N Root Mean Squared Error (RMSE) Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, '''Root Mean Squared Error (RMSE)''' is a widely used metric for evaluating the performance of regression models. It quantifies the difference between the predicted values and the true values by calculating the square root of the average of the squared differences. The RMSE is particularly useful because it gives a measure of error that is interpretable in the same unit as the original..." current
- 13:1313:13, 18 March 2023 diff hist +2,677 N Rectified Linear Unit (ReLU) Created page with "{{see also|Machine learning terms}} ==Rectified Linear Unit (ReLU)== The Rectified Linear Unit (ReLU) is a widely-used activation function in the field of machine learning and deep learning. It is a non-linear function that helps to model complex patterns and relationships in data. ReLU has gained significant popularity because of its simplicity and efficiency in training deep neural networks. ===History of ReLU=== The concept of ReLU can be traced back to t..." current
- 13:1313:13, 18 March 2023 diff hist +2,929 N ReLU Created page with "{{see also|Machine learning terms}} ==ReLU in Machine Learning== ReLU, or '''Rectified Linear Unit''', is a popular activation function used in artificial neural networks (ANNs) for implementing deep learning models. The primary role of an activation function is to introduce non-linearity in the model and improve its learning capability. ReLU has been widely adopted due to its simplicity, efficiency, and ability to mitigate the vanishing gradient problem...." current
- 13:1313:13, 18 March 2023 diff hist +3,770 N ROC (receiver operating characteristic) Curve Created page with "{{see also|Machine learning terms}} ==Introduction== The '''Receiver Operating Characteristic''' ('''ROC''') curve is a graphical representation that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied. It is widely used in machine learning, statistics, and data analysis for evaluating the performance of classification algorithms, particularly in the presence of imbalanced class distribution. ==Background== ===Origi..." current
- 13:1213:12, 18 March 2023 diff hist +4,269 N NLU Created page with "{{see also|Machine learning terms}} ==Introduction== Natural Language Understanding (NLU) is a subfield of Artificial Intelligence (AI) and Machine Learning (ML) that focuses on enabling computers to comprehend and interpret human language. This process includes the analysis of linguistic data to identify key elements such as entities, relations, and sentiments. NLU enables machines to understand the meaning and context of natural language input, allowing them to..." current
- 13:1213:12, 18 March 2023 diff hist +3,105 N N-gram Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning and natural language processing, an '''N-gram''' is a contiguous sequence of N items from a given sample of text or speech. N-grams are widely used for various tasks in computational linguistics, such as statistical language modeling, text classification, and information retrieval. The term "N-gram" is derived from the combination of the letter "N" and the word "gram," which originates..."
- 13:1213:12, 18 March 2023 diff hist +2,368 N Log Loss Created page with "{{see also|Machine learning terms}} ==Log Loss== Log Loss, also known as logarithmic loss or cross-entropy loss, is a common loss function used in machine learning for classification problems. It is a measure of the difference between the predicted probabilities and the true labels of a dataset. The Log Loss function quantifies the performance of a classifier by penalizing the predicted probabilities that deviate from the actual class labels. ==Usage in Machine Learning..." current
- 13:1213:12, 18 March 2023 diff hist +3,450 N LaMDA (Language Model for Dialogue Applications) Created page with "{{see also|Machine learning terms}} ==Introduction== '''LaMDA''' ('''L'''anguage '''M'''odel for '''D'''ialogue '''A'''pplications) is a conversational AI model developed by Google in the field of machine learning. LaMDA aims to improve the interaction between humans and computers by enabling open-domain conversations, thereby allowing machines to understand and respond to a wide range of topics. This article discusses the design, functionality, and key aspects of La..." current
- 13:1213:12, 18 March 2023 diff hist +3,193 N L2 regularization Created page with "{{see also|Machine learning terms}} ==Introduction== L2 regularization, also known as ridge regression or Tikhonov regularization, is a technique employed in machine learning to prevent overfitting and improve the generalization of a model. It is a form of regularization that adds a penalty term to the objective function, which helps in constraining the model's complexity. L2 regularization is particularly useful for linear regression models, but can also be appl..." current
- 13:1213:12, 18 March 2023 diff hist +2,352 N L2 loss Created page with "{{see also|Machine learning terms}} ==L2 Loss in Machine Learning== L2 Loss, also known as Euclidean Loss or Squared Error Loss, is a widely-used loss function in machine learning and deep learning. It is a popular choice for regression tasks, where the goal is to predict a continuous output value. L2 Loss quantifies the difference between the predicted output and the true output, providing a measure of model accuracy. ===Definition and Properties=== The L2 Loss is def..." current
- 13:1113:11, 18 March 2023 diff hist +3,011 N L1 regularization Created page with "{{see also|Machine learning terms}} ==L1 Regularization in Machine Learning== L1 regularization, also known as Lasso regularization or L1 norm, is a widely used regularization technique in machine learning and statistical modeling to prevent overfitting and enhance the generalization of the model. It achieves this by introducing a penalty term in the optimization objective that encourages sparsity in the model parameters. ===Overview=== Regularization techniques are emp..." current
- 13:1113:11, 18 March 2023 diff hist +3,112 N L1 loss Created page with "{{see also|Machine learning terms}} ==Introduction== In machine learning, various loss functions are used to measure the discrepancy between predicted values and actual values. L1 loss, also known as ''Least Absolute Deviations'' (LAD) or ''Least Absolute Errors'' (LAE), is one such loss function used in regression problems to estimate model parameters. L1 loss calculates the sum of absolute differences between predicted and actual values, making it robust to outliers an..." current
- 13:1113:11, 18 March 2023 diff hist +3,859 N GPT (Generative Pre-trained Transformer) Created page with "{{see also|Machine learning terms}} ==Introduction== The '''Generative Pre-trained Transformer''' ('''GPT''') is a series of machine learning models developed by OpenAI for natural language processing tasks. These models are based on the Transformer architecture introduced by Vaswani et al. in 2017. GPT models are designed to generate text by predicting subsequent words in a sequence, and have been applied to tasks such as text generation, translation, summarization,..." current
- 13:1113:11, 18 March 2023 diff hist +3,877 N BLEU (Bilingual Evaluation Understudy) Created page with "{{see also|Machine learning terms}} ==Introduction== The '''Bilingual Evaluation Understudy''' ('''BLEU''') is an automatic evaluation metric used in the field of Natural Language Processing (NLP) to measure the quality of machine-generated translations. Developed by IBM Research in 2002, it compares translations generated by a machine with a set of human-generated reference translations. BLEU scores are widely used in the evaluation of machine translation system..." current
- 13:1113:11, 18 March 2023 diff hist +3,640 N BERT (Bidirectional Encoder Representations from Transformers) Created page with "{{see also|Machine learning terms}} ==Introduction== BERT, or '''Bidirectional Encoder Representations from Transformers''', is a pre-training technique for natural language understanding tasks in the field of machine learning. Developed by researchers at Google AI Language, BERT has significantly advanced the state of the art in a wide range of tasks, such as question answering, sentiment analysis, and named entity recognition. BERT's breakthrough lies in its abilit..." current