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19:04, 18 March 2023 Walle talk contribs created page Trigram (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning and natural language processing (NLP), a '''trigram''' is a continuous sequence of three items from a given sample of text or speech. Trigrams are a type of n-gram, where ''n'' represents the number of items in the sequence. N-grams are used in various language modeling and feature extraction tasks to analyze and predict text data. ==Language Modeling== ===Probability Estimatio...")
19:04, 18 March 2023 Walle talk contribs created page Token (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, a '''token''' refers to a fundamental unit of text or data that is used for processing, analysis, or modeling. Tokens are essential components of natural language processing (NLP) systems, which aim to enable computers to understand, interpret, and generate human language. In this context, a token can represent a single word, a character, a subword, or any other unit of text that serve...")
19:03, 18 March 2023 Walle talk contribs created page Out-of-bag evaluation (OOB evaluation) (Created page with "{{see also|Machine learning terms}} ==Out-of-Bag Evaluation== Out-of-Bag (OOB) evaluation is a model validation technique commonly used in ensemble learning methods, particularly in bagging algorithms such as Random Forests. The main idea behind OOB evaluation is to use a portion of the training data that was not used during the construction of individual base learners, for the purpose of estimating the performance of the ensemble without resorting to a separ...")
19:03, 18 March 2023 Walle talk contribs created page Oblique condition (Created page with "{{see also|Machine learning terms}} ==Oblique Condition in Machine Learning== The oblique condition refers to a specific type of decision boundary used in machine learning algorithms, particularly in classification tasks. Decision boundaries are mathematical functions or models that separate different classes or categories in the input data. Oblique decision boundaries are characterized by their non-orthogonal orientation, allowing for more complex and flexible separatio...")
19:03, 18 March 2023 Walle talk contribs created page Non-binary condition (Created page with "{{see also|Machine learning terms}} ==Introduction== In the context of machine learning, the term "non-binary condition" refers to a situation where the output or target variable of a predictive model is not restricted to two distinct classes or labels. This contrasts with binary classification tasks, where the goal is to predict one of two possible outcomes. Non-binary conditions arise in various types of problems, such as multi-class classification, multi-label classif...")
19:03, 18 March 2023 Walle talk contribs created page Node (decision tree) (Created page with "{{see also|Machine learning terms}} ==Definition== In machine learning, a '''node''' refers to a point within a decision tree at which a decision is made based on the input data. Decision trees are hierarchical, tree-like structures used to model decisions and their possible consequences, including the chance event outcomes, resource costs, and utility. Nodes in decision trees can be of three types: root node, internal node, and leaf node. ===Root Node=== The ''...")
19:03, 18 March 2023 Walle talk contribs created page Leaf (Created page with "{{see also|Machine learning terms}} ==Introduction== In machine learning, a '''leaf''' is an essential component of decision tree-based algorithms, such as decision trees, random forests, and gradient boosting machines. A leaf, also known as a terminal node, is the endpoint of a branch in a decision tree, which is used to make predictions based on a set of input features. In this article, we will discuss the concept of leaves, their role in decision tree-...")
19:03, 18 March 2023 Walle talk contribs created page Information gain (Created page with "{{see also|Machine learning terms}} ==Information Gain in Machine Learning== Information gain is a crucial concept in the field of machine learning, particularly when dealing with decision trees and feature selection. It is a metric used to measure the decrease in uncertainty or entropy after splitting a dataset based on a particular attribute. The primary goal of information gain is to identify the most informative attribute, which can be used to construct an effect...")
19:03, 18 March 2023 Walle talk contribs created page Inference path (Created page with "{{see also|Machine learning terms}} ==Inference Path in Machine Learning== The '''inference path''' in machine learning refers to the process of applying a trained model to new, unseen data in order to make predictions or decisions. This process is critical in realizing the practical applications of machine learning models, as it enables them to generalize their learned knowledge to real-world situations. ==Training and Inference Phases== Machine learning models typical...")
19:02, 18 March 2023 Walle talk contribs created page In-set condition (Created page with "{{see also|Machine learning terms}} ==In-set Condition in Machine Learning== The in-set condition is a concept in the field of machine learning that refers to the circumstance in which the training data used to train a machine learning model is representative of the data distribution that the model will encounter during real-world applications. This concept is related to the generalization performance of a model, which refers to its ability to perform well on unseen...")
19:02, 18 March 2023 Walle talk contribs created page Gradient boosting (Created page with "{{see also|Machine learning terms}} ==Introduction== Gradient boosting is a popular and powerful machine learning algorithm used for both classification and regression tasks. It belongs to the family of ensemble learning methods, which combine the predictions of multiple base models to produce a more accurate and robust prediction. The main idea behind gradient boosting is to sequentially add weak learners (typically decision trees) to the ensemble, each...")
19:02, 18 March 2023 Walle talk contribs created page Gradient boosted (decision) trees (GBT) (Created page with "{{see also|Machine learning terms}} ==Introduction== Gradient Boosted Trees (GBT), also known as Gradient Boosted Decision Trees or Gradient Boosting Machines, is a powerful ensemble learning technique in the field of machine learning. GBT constructs an ensemble of weak learners, typically decision trees, in a sequential manner, with each tree optimizing the model's performance by minimizing the error made by the previous tree. The technique is particularly well-suited f...")
19:02, 18 March 2023 Walle talk contribs created page Gini impurity (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, Gini impurity is a metric used to measure the impurity or disorder within a dataset. It is commonly employed in decision tree algorithms, such as the Classification and Regression Tree (CART) algorithm, to decide the best splitting points for nodes. The Gini impurity index quantifies the probability of misclassification by calculating the degree of purity in a dataset, which he...")
19:02, 18 March 2023 Walle talk contribs created page Feature importances (Created page with "{{see also|Machine learning terms}} ==Introduction== Feature importances refer to the quantification of the relative contribution of each feature (or input variable) to the overall predictive performance of a machine learning model. Identifying and understanding the importance of features in a model can aid in model interpretation, feature selection, and ultimately, the improvement of model performance. Various techniques have been proposed to assess the significance...")
19:02, 18 March 2023 Walle talk contribs created page Entropy (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, entropy is a fundamental concept that is derived from information theory. It is used to measure the impurity or randomness in a set of data. Entropy has various applications in machine learning, such as decision tree construction, feature selection, and information gain calculation. Understanding entropy and its implications is essential for designing and implementing effective mac...")
19:01, 18 March 2023 Walle talk contribs created page Decision tree (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, a '''decision tree''' is a popular and widely used model that helps in making predictions based on a series of decisions. The decision tree model can be used for both classification and regression tasks, and it works by recursively splitting the input data into subsets based on the values of the input features, ultimately making a prediction. ==Structure of a Decision Tree== ===No...")
19:01, 18 March 2023 Walle talk contribs created page Decision forest (Created page with "{{see also|Machine learning terms}} ==Introduction== A '''decision forest''' (also known as a '''random forest''') is an ensemble learning method in machine learning that combines multiple decision trees to generate a more accurate and robust prediction model. This method is widely used in classification and regression tasks, and it can handle both categorical and numerical input features. Decision forests are known for their ability to mitigate overfitting and improve g...")
19:01, 18 March 2023 Walle talk contribs created page Condition (Created page with "{{see also|Machine learning terms}} ==Introduction== In machine learning, the term "condition" typically refers to a criterion or a set of criteria that must be met for a specific event to occur or an action to be taken. Conditions are used in various aspects of machine learning, including decision trees, rule-based systems, and optimization algorithms. This article aims to provide an understanding of conditions in machine learning and their significance, as well as...")
19:01, 18 March 2023 Walle talk contribs created page Binary condition (Created page with "{{see also|Machine learning terms}} ==Binary Condition in Machine Learning== In the field of machine learning, a '''binary condition''' refers to a specific type of classification problem where the target variable consists of only two distinct classes or categories. These types of problems are often encountered in various applications, such as spam detection, medical diagnosis, and sentiment analysis. The primary goal of binary classification models is to correctly p...")
19:01, 18 March 2023 Walle talk contribs created page Bagging (Created page with "{{see also|Machine learning terms}} ==Bagging in Machine Learning== Bagging, or '''Bootstrap Aggregating''', is a popular ensemble learning technique in machine learning that aims to improve the stability and accuracy of a base learning algorithm by training multiple instances of the same model on different subsamples of the training data. The predictions from the individual models are then combined, usually by means of a majority vote, to produce the final output. This...")
19:01, 18 March 2023 Walle talk contribs created page 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...")
19:00, 18 March 2023 Walle talk contribs created page 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...")
13:30, 18 March 2023 Walle talk contribs created page 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...")
13:29, 18 March 2023 Walle talk contribs created page 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...")
13:29, 18 March 2023 Walle talk contribs created page 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...")
13:29, 18 March 2023 Walle talk contribs created page 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...")
13:29, 18 March 2023 Walle talk contribs created page 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...")
13:29, 18 March 2023 Walle talk contribs created page 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...")
13:29, 18 March 2023 Walle talk contribs created page 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...")
13:29, 18 March 2023 Walle talk contribs created page 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...")
13:28, 18 March 2023 Walle talk contribs created page 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. =...")
13:28, 18 March 2023 Walle talk contribs created page 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...")
13:28, 18 March 2023 Walle talk contribs created page 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...")
13:28, 18 March 2023 Walle talk contribs created page 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...")
13:28, 18 March 2023 Walle talk contribs created page 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...")
13:28, 18 March 2023 Walle talk contribs created page 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)...")
13:27, 18 March 2023 Walle talk contribs created page 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...")
13:27, 18 March 2023 Walle talk contribs created page 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...")
13:27, 18 March 2023 Walle talk contribs created page 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....")
13:27, 18 March 2023 Walle talk contribs created page 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...")
13:27, 18 March 2023 Walle talk contribs created page 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,...")
13:27, 18 March 2023 Walle talk contribs created page 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...")
13:26, 18 March 2023 Walle talk contribs created page 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...")
13:26, 18 March 2023 Walle talk contribs created page 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...")
13:26, 18 March 2023 Walle talk contribs created page 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...")
13:26, 18 March 2023 Walle talk contribs created page 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...")
13:26, 18 March 2023 Walle talk contribs created page 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...")
13:26, 18 March 2023 Walle talk contribs created page 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...")
13:26, 18 March 2023 Walle talk contribs created page 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...")
13:25, 18 March 2023 Walle talk contribs created page 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...")