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11:44, 20 March 2023 Walle talk contribs created page Non-response bias (Created page with "{{see also|Machine learning terms}} ==Non-response Bias in Machine Learning== Non-response bias, a type of sampling bias, occurs in machine learning when the data used for training and evaluating a model fails to accurately represent the entire population due to the absence or underrepresentation of certain subgroups in the sample. This phenomenon can lead to poor generalization performance, as the model's predictions may be systematically biased and not applicable t...")
11:44, 20 March 2023 Walle talk contribs created page Noise (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, noise refers to the presence of unwanted or irrelevant data that can have a detrimental effect on the performance and accuracy of a model. Noise can be introduced during the data collection process, data preprocessing, or through inherent randomness in the data itself. This article will provide an overview of the various types of noise, their sources, and their impacts on machine l...")
11:44, 20 March 2023 Walle talk contribs created page Node (TensorFlow graph) (Created page with "{{see also|Machine learning terms}} ==Node (TensorFlow graph)== In the context of machine learning, a node is a fundamental unit within a computational graph, which is a directed, acyclic graph (DAG) used to represent the flow of data and operations in a TensorFlow model. A TensorFlow graph is composed of multiple nodes, each representing an operation or a variable, which are connected by edges representing the flow of data between these nodes. The TensorFlow graph is a...")
11:44, 20 March 2023 Walle talk contribs created page Multinomial regression (Created page with "{{see also|Machine learning terms}} ==Multinomial Regression== Multinomial regression, also known as multinomial logistic regression or softmax regression, is a statistical method used in machine learning and statistics for modeling the relationship between a categorical dependent variable and one or more independent variables. It is an extension of binary logistic regression, which is used for predicting binary outcomes. Multinomial regression is particularly us...")
11:44, 20 March 2023 Walle talk contribs created page Multinomial classification (Created page with "{{see also|Machine learning terms}} ==Multinomial Classification== Multinomial classification, also known as multi-class or multi-nominal classification, is a type of supervised machine learning problem where the objective is to categorize an input data point into one of several discrete classes. In contrast to binary classification, where there are only two possible categories, multinomial classification deals with three or more categories. ===Problem Formulation==...")
11:43, 20 March 2023 Walle talk contribs created page Multi-class logistic regression (Created page with "{{see also|Machine learning terms}} ==Introduction== '''Multi-class logistic regression''', also referred to as '''softmax regression''' or '''multinomial logistic regression''', is a supervised machine learning algorithm used for predicting the categorical label of an input instance when there are more than two possible classes. It is an extension of the binary logistic regression model, which can only handle two-class classification problems. Multi-class logistic r...")
11:43, 20 March 2023 Walle talk contribs created page Model training (Created page with "{{see also|Machine learning terms}} ==Introduction== Model training in machine learning refers to the process of developing a mathematical model capable of making predictions or decisions based on input data. This is achieved by iteratively adjusting the model's parameters until it can accurately generalize from the training data to previously unseen data. The ultimate goal of this process is to create a model that can perform well on new, real-world data without bei...")
11:43, 20 March 2023 Walle talk contribs created page Model capacity (Created page with "{{see also|Machine learning terms}} ==Definition== In the context of machine learning, ''model capacity'' refers to the ability of a model to learn and represent various functions and patterns within a given dataset. High-capacity models have a larger number of parameters and can therefore represent more complex functions, while low-capacity models have fewer parameters and are limited in the complexity of functions they can represent. Model capacity plays a crucial role...")
11:43, 20 March 2023 Walle talk contribs created page Minimax loss (Created page with "{{see also|Machine learning terms}} ==Minimax Loss== The minimax loss, also known as the minimax regret, is a performance measure in machine learning and game theory that quantifies the worst-case performance of an algorithm or decision rule under uncertainty. This concept is utilized in various optimization problems, where the goal is to minimize the maximum possible loss or regret under uncertain conditions. ===Definition=== Given a decision-making problem, th...")
11:43, 20 March 2023 Walle talk contribs created page Mini-batch stochastic gradient descent (Created page with "{{see also|Machine learning terms}} ==Introduction== In machine learning, '''mini-batch stochastic gradient descent''' ('''MB-SGD''') is an optimization algorithm commonly used for training neural networks and other models. The algorithm operates by iteratively updating model parameters to minimize a loss function, which measures the discrepancy between the model's predictions and actual target values. Mini-batch stochastic gradient descent is a variant of stochastic g...")
11:43, 20 March 2023 Walle talk contribs created page Metric (Created page with "{{see also|Machine learning terms}} ==Introduction== In machine learning, a '''metric''' refers to a quantitative measure that is used to evaluate the performance of an algorithm or model. Metrics help researchers and practitioners understand the effectiveness of their models in solving a particular task and allow for comparison with other models. Several types of metrics exist, each tailored to different types of tasks or problems, such as classification, regression...")
11:42, 20 March 2023 Walle talk contribs created page Matrix factorization (Created page with "{{see also|Machine learning terms}} ==Introduction== Matrix factorization is a technique in machine learning that aims to discover latent features underlying the interactions between two different kinds of entities. It has been widely used for tasks such as recommendation systems, dimensionality reduction, and data imputation. The primary goal of matrix factorization is to approximate a given matrix by factorizing it into two or more lower-dimensional matrices, which can...")
11:42, 20 March 2023 Walle talk contribs created page Matplotlib (Created page with "{{see also|Machine learning terms}} ==Introduction== '''Matplotlib''' is a widely used data visualization library in Python that enables developers to create high-quality and interactive visualizations, such as line plots, scatter plots, bar plots, histograms, 3D plots, and more. It is an essential tool in machine learning and data science for exploring and analyzing data, as well as presenting the results of models and algorithm...")
11:42, 20 March 2023 Walle talk contribs created page Loss surface (Created page with "{{see also|Machine learning terms}} ==Loss Surface in Machine Learning== In the field of machine learning, the '''loss surface''' (also referred to as the '''error surface''' or the '''objective function surface''') refers to the graphical representation of the relationship between the parameters of a learning model and the associated loss or error. The primary goal of machine learning algorithms is to optimize these parameters, minimizing the loss and consequently e...")
11:42, 20 March 2023 Walle talk contribs created page NumPy (Created page with "{{see also|Machine learning terms}} ==Introduction== NumPy (Numerical Python) is a highly popular and widely used open-source library in the field of machine learning and data science. NumPy provides a variety of tools and functions for working with numerical data in the Python programming language. It is highly regarded for its efficiency, simplicity, and performance in handling multi-dimensional arrays and matrices, as well as for its comprehensive suite of...")
11:42, 20 March 2023 Walle talk contribs created page NaN trap (Created page with "{{see also|Machine learning terms}} ==NaN Trap in Machine Learning== NaN trap, short for 'Not a Number' trap, is a common issue encountered in machine learning algorithms, particularly during the training process. It occurs when the algorithm's calculations yield undefined or unrepresentable numerical results, leading to the propagation of NaN values throughout the model. This can significantly hinder the model's learning capability and adversely affect its performance....")
11:42, 20 March 2023 Walle talk contribs created page Momentum (Created page with "{{see also|Machine learning terms}} ==Momentum in Machine Learning== Momentum is a widely-used optimization technique in the field of machine learning and deep learning, specifically in training neural networks. This method aims to accelerate the convergence of gradient-based optimization algorithms such as gradient descent and stochastic gradient descent by incorporating information from previous iterations. ===Gradient Descent and Stochastic Gradient Descent==...")
11:41, 20 March 2023 Walle talk contribs created page Metrics API (tf.metrics) (Created page with "{{see also|Machine learning terms}} ==Overview== The '''Metrics API''' in machine learning, specifically in the context of ''TensorFlow'' (TensorFlow), is a collection of utilities and classes designed to compute and represent various evaluation metrics, also known as performance metrics. These metrics are essential for evaluating the performance of machine learning models, and the Metrics API, referred to as '''tf.metrics''' in TensorFlow, facilitates the calculatio...")
11:41, 20 March 2023 Walle talk contribs created page Mean Squared Error (MSE) (Created page with "{{see also|Machine learning terms}} ==Mean Squared Error (MSE)== Mean Squared Error (MSE) is a widely used metric to evaluate the performance of regression models in machine learning. It measures the average of the squared differences between the predicted values and the actual values. MSE is suitable for evaluating continuous variables and is particularly useful when dealing with datasets that include outliers, as it emphasizes larger errors due to the squaring operatio...")
11:41, 20 March 2023 Walle talk contribs created page Mean Absolute Error (MAE) (Created page with "{{see also|Machine learning terms}} ==Mean Absolute Error (MAE)== The '''Mean Absolute Error (MAE)''' is a widely used metric in Machine Learning and Statistics to evaluate the performance of a predictive model. It measures the average magnitude of errors between the predicted and actual values, without considering the direction of the errors. MAE is a popular choice for regression tasks as it provides an easily interpretable representation of the model's error....")
05:06, 20 March 2023 Walle talk contribs created page Logits (Created page with "{{see also|Machine learning terms}} ==Logits in Machine Learning== In the field of machine learning, logits refer to the unnormalized probability values that are output by a classification model before they are transformed into actual probabilities. Logits are often associated with neural networks, particularly in the context of deep learning and artificial intelligence. These values serve as a crucial intermediate step in the process of predicting class prob...")
05:06, 20 March 2023 Walle talk contribs created page Least squares regression (Created page with "{{see also|Machine learning terms}} ==Introduction== In machine learning, '''Least Squares Regression''' is a well-established method for fitting a linear model to a set of data points. It seeks to minimize the sum of the squared differences between the observed values and the values predicted by the linear model. This technique is particularly useful in applications where the relationship between input features and the target variable is linear or near-linear. In this a...")
05:05, 20 March 2023 Walle talk contribs created page Items (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, the term "items" typically refers to individual data points or instances that are used as inputs to train, validate, or test machine learning models. Items can take various forms, such as images, texts, or numerical values, depending on the specific problem being addressed. This article will explore the concept of items in machine learning, their significance in model training, and the...")
05:05, 20 March 2023 Walle talk contribs created page Item matrix (Created page with "{{see also|Machine learning terms}} ==Item Matrix in Machine Learning== The Item Matrix is a fundamental concept in machine learning, particularly in the realm of collaborative filtering and recommendation systems. It is a structured representation of items and their features, allowing algorithms to analyze patterns and provide personalized recommendations to users. This article delves into the definition, purpose, and applications of an Item Matrix, and offers a simplif...")
05:05, 20 March 2023 Walle talk contribs created page Inter-rater agreement (Created page with "{{see also|Machine learning terms}} ==Introduction== Inter-rater agreement, also referred to as inter-rater reliability or inter-annotator agreement, is a measure of the degree of consistency or consensus among multiple raters or annotators when evaluating a set of items, such as classifying data points in a machine learning task. This measure is essential in various machine learning and natural language processing (NLP) applications, where human-annotated data i...")
05:05, 20 March 2023 Walle talk contribs created page Instance (Created page with "{{see also|Machine learning terms}} ==Definition of Instance in Machine Learning== An '''instance''' in machine learning refers to a single data point or example used in the process of training and evaluating machine learning models. Instances are essential components of the dataset and are typically represented as a set of features and their corresponding labels or target values. They serve as the basis for learning patterns, making predictions, and evaluating the p...")
05:05, 20 March 2023 Walle talk contribs created page Individual fairness (Created page with "{{see also|Machine learning terms}} ==Individual Fairness in Machine Learning== Individual fairness in machine learning refers to the concept of ensuring that similar individuals are treated similarly by a machine learning algorithm. This idea has gained significant attention in recent years due to concerns about the potential for algorithmic bias and unfair treatment of individuals in various domains, including finance, healthcare, criminal justice, and hiring practices...")
05:05, 20 March 2023 Walle talk contribs created page Independently and identically distributed (i.i.d) (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, the concept of ''independently and identically distributed'' (i.i.d) refers to a fundamental assumption about the nature of data used in statistical modeling and analysis. The i.i.d assumption is particularly important in the development of machine learning algorithms and their evaluation, as it affects the validity of the models and the accuracy of their predictions. The i.i.d assumpt...")
05:05, 20 March 2023 Walle talk contribs created page Incompatibility of fairness metrics (Created page with "{{see also|Machine learning terms}} ==Incompatibility of Fairness Metrics in Machine Learning== In the field of machine learning, fairness is a critical issue that has gained increasing attention in recent years. The concept of fairness is essential to ensure that algorithmic decisions are equitable and do not discriminate against particular groups. This article focuses on the incompatibility of fairness metrics in machine learning, its implications, and a simple explana...")
05:04, 20 March 2023 Walle talk contribs created page In-group bias (Created page with "{{see also|Machine learning terms}} ==In-group Bias in Machine Learning== In-group bias, also referred to as in-group favoritism or in-group preference, is a well-established phenomenon in social psychology. It occurs when individuals show a preference for members of their own group over those of other groups. In the context of machine learning, in-group bias refers to the unintentional favoring of specific groups in the algorithmic decision-making process, often resulti...")
05:04, 20 March 2023 Walle talk contribs created page Implicit bias (Created page with "{{see also|Machine learning terms}} ==Introduction== Implicit bias in machine learning refers to the unintentional introduction of discriminatory or prejudiced behaviors, patterns, or outcomes in machine learning models, primarily due to the influence of biased training data or algorithmic design. These biases may manifest in the form of unfair treatment of certain demographic groups, perpetuation of stereotypes, or unequal allocation of resources. As machine learning sy...")
05:04, 20 March 2023 Walle talk contribs created page Hyperplane (Created page with "{{see also|Machine learning terms}} ==Definition== In machine learning, a '''hyperplane''' refers to a geometric construct that serves as a decision boundary for separating different classes or categories of data points in a multidimensional space. It is an essential concept for many classification and regression algorithms, including the popular Support Vector Machines (SVM) method. Mathematically, a hyperplane is an (n-1)-dimensional subspace within an n-dimens...")
05:04, 20 March 2023 Walle talk contribs created page Holdout data (Created page with "{{see also|Machine learning terms}} ==Holdout Data in Machine Learning== Holdout data is a subset of the dataset in machine learning that is separated from the training data and is used to evaluate the performance of a model during the model selection process. Holdout data helps to identify potential issues such as overfitting and provides an unbiased estimate of the model's generalization performance. This section discusses the importance of holdout data, the pr...")
05:04, 20 March 2023 Walle talk contribs created page Hinge loss (Created page with "{{see also|Machine learning terms}} ==Hinge Loss== Hinge loss is a type of loss function used in machine learning and specifically in support vector machines (SVMs). It measures the error between the predicted output and the actual output for a given training example. Hinge loss is particularly effective for binary classification problems, as it aims to find the optimal decision boundary (or margin) that maximally separates two classes of data points. ===Definit...")
05:04, 20 March 2023 Walle talk contribs created page Heuristic (Created page with "{{see also|Machine learning terms}} ==Definition of Heuristic== Heuristics, derived from the Greek word ''heuriskein'' which means "to discover," are problem-solving techniques that employ a practical approach to finding an adequate, though not always optimal, solution to complex problems. In machine learning, heuristics are often utilized to guide the search for an appropriate model or to optimize algorithmic parameters when an exhaustive search is computationally i...")
05:03, 20 March 2023 Walle talk contribs created page Hashing (Created page with "{{see also|Machine learning terms}} ==Hashing in Machine Learning== Hashing, a technique commonly used in computer science, has found various applications in the field of machine learning. In this context, hashing mainly refers to the process of converting high-dimensional input data into lower-dimensional representations, while preserving important information about the original data. This transformation can be beneficial for numerous machine learning tasks, including f...")
05:03, 20 March 2023 Walle talk contribs created page Hallucination (Created page with "{{see also|Machine learning terms}} ==Hallucination in Machine Learning== Hallucination in machine learning refers to the phenomenon where a model generates outputs that are not entirely accurate or relevant to the input data. This occurs when the model overfits to the training data or does not generalize well to new or unseen data. This behavior has been observed in various machine learning models, including deep learning models like neural networks and natural lang...")
05:03, 20 March 2023 Walle talk contribs created page Group attribution bias (Created page with "{{see also|Machine learning terms}} ==Introduction== Group attribution bias is a term used to describe a phenomenon in machine learning where an algorithm systematically and unfairly associates certain characteristics or outcomes with specific groups of individuals. This bias often stems from the training data that a machine learning model is exposed to, which may contain unrepresentative or skewed examples. When a model is trained on such data, it may inadvertently lear...")
05:03, 20 March 2023 Walle talk contribs created page Graph execution (Created page with "{{see also|Machine learning terms}} ==Graph Execution in Machine Learning== Graph execution in machine learning refers to a computational paradigm that employs directed graphs to represent and execute complex operations and dependencies between data, models, and algorithms. The graph execution approach is typically used in conjunction with TensorFlow, a popular open-source machine learning library, to optimize performance and parallelism in deep learning models. It p...")
05:03, 20 March 2023 Walle talk contribs created page Graph (Created page with "{{see also|Machine learning terms}} ==Introduction== In the context of machine learning, a '''graph''' is a mathematical structure that represents relationships between objects or entities, typically in the form of nodes (or vertices) connected by edges (or links). Graphs have become increasingly popular in recent years due to their ability to represent complex data and their effectiveness in solving various machine learning tasks. They are particularly useful for repres...")
05:03, 20 March 2023 Walle talk contribs created page Layers API (tf.layers) (Created page with "{{see also|Machine learning terms}} ==Introduction== The '''Layers API''' (commonly referred to as '''tf.layers''') is a high-level interface within the TensorFlow machine learning framework, specifically designed to simplify the process of building and training neural networks. It provides pre-built, reusable components, known as layers, that can be easily combined and customized to create a wide range of machine learning models. The Layers API encourages modular de...")
05:02, 20 March 2023 Walle talk contribs created page Kernel Support Vector Machines (KSVMs) (Created page with "{{see also|Machine learning terms}} ==Introduction== Kernel Support Vector Machines (KSVMs) are a class of machine learning algorithms that are particularly well-suited for classification and regression tasks. They are an extension of the Support Vector Machine (SVM) algorithm and utilize kernel functions to project data into a higher-dimensional space, allowing for nonlinear decision boundaries. This article aims to provide an academic-style overview of the key...")
05:02, 20 March 2023 Walle talk contribs created page Keras (Created page with "{{see also|Machine learning terms}} ==Introduction== Keras is an open-source, high-level neural networks API (Application Programming Interface) designed to simplify the process of building and training deep learning models. It was developed by François Chollet and released in March 2015. Keras acts as a user-friendly interface to the more complex deep learning libraries like TensorFlow, Theano, and Microsoft Cognitive Toolkit (CNTK). By providin...")
05:02, 20 March 2023 Walle talk contribs created page IoU (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning and computer vision, '''Intersection over Union (IoU)''' is a commonly used evaluation metric for object detection and segmentation tasks. This metric measures the overlap between the predicted bounding box (or segmentation mask) and the ground truth bounding box (or segmentation mask), providing a quantitative assessment of the model's performance in terms of its ability to acc...")
03:42, 20 March 2023 Alpha5 talk contribs created page GPT-4 (Created page with "==Exams== {| class="wikitable" ! Exam ! GPT-4 Estimated Percentile ! GPT-4 (no vision) Estimated Percentile ! GPT-3.5 Estimated Percentile |- | Uniform Bar Exam (MBE+MEE+MPT)1 | 298 / 400 (~90th) | 298 / 400 (~90th) | 213 / 400 (~10th) |- | LSAT | 163 (~88th) | 161 (~83rd) | 149 (~40th) |- | SAT Evidence-Based Reading & Writing | 710 / 800 (~93rd) | 710 / 800 (~93rd) | 670 / 800 (~87th) |- | SAT Math | 700 / 800 (~89th) | 690 / 800 (~89th)...")
01:19, 20 March 2023 Walle talk contribs created page Gradient (Created page with "{{see also|Machine learning terms}} ==Gradient in Machine Learning== Gradient is a fundamental concept in machine learning, particularly in the optimization process of training algorithms. It is a multi-dimensional generalization of the derivative, which quantifies the rate of change of a function with respect to its variables. In machine learning, the gradient is typically used to minimize a loss function, which measures the discrepancy between the predictions made by t...")
01:18, 20 March 2023 Walle talk contribs created page Generator (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, a '''generator''' refers to a model or algorithm that generates new data samples, which can be either synthetic or based on existing data. Generators have become increasingly popular with the advent of generative models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), which are capable of generating complex data distributions. These models ha...")
01:18, 20 March 2023 Walle talk contribs created page Generative model (Created page with "{{see also|Machine learning terms}} ==Introduction== A generative model is a type of machine learning algorithm that aims to learn the underlying probability distribution of the training data in order to generate new data samples that resemble the original dataset. These models have been widely adopted in various applications such as natural language processing, image synthesis, and anomaly detection. ==Types of Generative Models== Generative models can be broadly c...")
01:18, 20 March 2023 Walle talk contribs created page Generative adversarial network (GAN) (Created page with "{{see also|Machine learning terms}} ==Introduction== A '''Generative Adversarial Network''' ('''GAN''') is a type of machine learning algorithm developed by Ian Goodfellow and his colleagues in 2014<ref>Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. In ''Advances in neural information processing systems'' (pp. 2672-2680).</ref>. GANs are comprised of two distinct...")
01:18, 20 March 2023 Walle talk contribs created page Generalized linear model (Created page with "{{see also|Machine learning terms}} ==Generalized Linear Models (GLMs)== Generalized Linear Models (GLMs) are a class of statistical models that extend the linear regression model, allowing for response variables with distributions other than the normal distribution. GLMs were first introduced by John Nelder and Robert Wedderburn in 1972, and have since become a fundamental tool in statistical modeling and machine learning. ===Components of a Generalized Linear Mode...")