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