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Combined display of all available logs of AI Wiki. You can narrow down the view by selecting a log type, the username (case-sensitive), or the affected page (also case-sensitive).

• 08:42, 22 February 2023 Alpha5 talk contribs created page TPR (Redirected page to True positive rate (TPR)) Tag: New redirect
• 08:42, 22 February 2023 Alpha5 talk contribs created page True positive rate (Redirected page to True positive rate (TPR)) Tag: New redirect
• 08:42, 22 February 2023 Alpha5 talk contribs created page True positive rate (TPR) (Created page with "{{see also|Machine learning terms}} ===Introduction== Machine learning demands the evaluation of a model's performance. One way to do this is through classification metrics like true positive rate (TPR). TPR measures how well a model can correctly identify positive instances. In this article, we'll define TPR and discuss its significance in machine learning. ==Definition== True Positive Rate (TPR) is a classification metric that measures the proportion of actual positiv...")
• 06:28, 22 February 2023 Alpha5 talk contribs created page HF (Redirected page to Hugging Face) Tag: New redirect
• 05:13, 22 February 2023 Alpha5 talk contribs created page TP (Redirected page to True positive (TP)) Tag: New redirect
• 05:13, 22 February 2023 Alpha5 talk contribs created page True positive (Redirected page to True positive (TP)) Tag: New redirect
• 05:12, 22 February 2023 Alpha5 talk contribs created page True positive (TP) (Created page with "===Introduction== Machine learning classification is the process of classifying data into distinct classes. When assessing a model's performance, it is essential to assess its capacity for correctly predicting each data instance's class. One important evaluation metric used in binary classification is True Positive (TP), which measures how many positive samples are correctly classified as positive by the model. ==What is True Positive?== True Positive is a statistic use...")
• 04:55, 22 February 2023 Alpha5 talk contribs created page Machine learning model (Redirected page to Models) Tag: New redirect
• 04:50, 22 February 2023 Alpha5 talk contribs created page True negative (TN) (Created page with "===Introduction== Machine learning classification is the process of accurately predicting a data point's class based on features. This classification can lead to four distinct outcomes: true positive (TP), true negative (TN), false positive (FP) and false negative (FN). In this article, we'll focus on the true negative (TN) outcome. ==What is True Negative (TN)?== True Negative (TN) is one of the possible outcomes in a binary classification problem when the model predic...")
• 04:49, 22 February 2023 Alpha5 talk contribs created page TN (Redirected page to True negative (TN)) Tag: New redirect
• 04:49, 22 February 2023 Alpha5 talk contribs created page True negative (Redirected page to True negative (TN)) Tag: New redirect
• 04:21, 22 February 2023 Alpha5 talk contribs created page Training set (Created page with "===Introduction== Machine learning relies on access to large amounts of data in order to develop models that accurately predict outcomes. This set, known as the training set, helps train the model so it can recognize patterns and make predictions based on newly added information. ==What is a Training Set?== A training set is a collection of data used to train a machine learning model. This set typically contains examples of inputs and outputs that the model uses to lear...")
• 04:21, 22 February 2023 Alpha5 talk contribs created page Training data (Redirected page to Training set) Tag: New redirect
• 01:58, 22 February 2023 Alpha5 talk contribs created page Online model (Redirected page to Dynamic model) Tag: New redirect
• 01:43, 22 February 2023 Alpha5 talk contribs created page Dynamic model (Created page with "===Introduction== Machine learning is an ever-evolving field that utilizes mathematical algorithms and statistical models to empower computer systems to learn from data and make decisions. A dynamic model in machine learning refers to a type of model that can adjust its behavior over time in response to changes in its environment or new information. Dynamic models are especially beneficial in situations where the environment or data being used to train a model are const...")
• 01:15, 22 February 2023 Alpha5 talk contribs created page Early stoppage (Redirected page to Early stopping) Tag: New redirect
• 01:04, 22 February 2023 Alpha5 talk contribs created page Early stopping (Created page with "===Introduction== Machine learning seeks to train a model that can make accurate predictions on new data. Unfortunately, during training it is common for the model to overfit the training data; that is, it becomes too complex and includes irrelevant details or noise in the dataset. Unfortunately, overfitting can lead to poor performance when faced with new scenarios - thus defeating its purpose. Early stopping is an approach used in machine learning to prevent overfittin...")
• 21:44, 21 February 2023 Alpha5 talk contribs created page Online (Redirected page to Dynamic) Tag: New redirect
• 21:33, 21 February 2023 Alpha5 talk contribs created page Dynamic (Created page with "==Introduction== Machine learning is an ever-evolving field that utilizes mathematical algorithms and statistical models to empower computer systems to learn from data and make decisions. A dynamic model in machine learning refers to a type of model that can adjust its behavior over time in response to changes in its environment or new information. Dynamic models are especially beneficial in situations where the environment or data being used to train a model are consta...")
• 21:24, 21 February 2023 Alpha5 talk contribs created page Categorical features (Redirected page to Discrete feature) Tag: New redirect
• 21:23, 21 February 2023 Alpha5 talk contribs created page Categorical feature (Redirected page to Discrete feature) Tag: New redirect
• 21:09, 21 February 2023 Alpha5 talk contribs created page Discrete feature (Created page with "===Introduction== Machine learning uses features, or characteristics or attributes of input data, as a basis for making predictions or decisions. Discrete features (also referred to as categorical features) are those which take on a limited set of values rather than providing an infinite range of values. ==Definition== Discrete features refer to data elements whose values fall outside a finite or infinite set. Examples of discrete features include gender, hair color, oc...")
• 13:26, 21 February 2023 Alpha5 talk contribs created page Depth (Created page with "===Introduction== Depth is an essential concept in machine learning, particularly deep learning, where it refers to the number of layers within a neural network. Neural networks consist of interconnected artificial neurons that process and transform data. The depth of a network is determined by its number of layers and has an immense effect on its performance; more layers equals greater complexity for your model. ==What is depth in machine learning?== Machine learning e...")
• 13:22, 21 February 2023 Alpha5 talk contribs created page Dense feature (Created page with "===Introduction== Machine learning takes advantage of datasets that contain various features which can be utilized to make predictions about an outcome of interest. Features are the individual measurements or attributes assigned to each instance in a dataset; dense features in particular are often employed in this process. ==Definition of Dense Feature== Dense features in machine learning refer to those with a high-dimensional vector representation, where each dimension...")
• 13:10, 21 February 2023 Alpha5 talk contribs created page Deep model (Created page with "===Introduction== In machine learning, a deep model is an artificial neural network composed of multiple layers. These networks are designed to learn representations of data that become increasingly abstract and complex as it progresses through each layer. Deep models have been employed in order to achieve top-of-the-art performance on various tasks such as image and speech recognition, natural language processing, and game playing. ==Background== Artificial neural netw...")
• 12:48, 21 February 2023 Alpha5 talk contribs created page Model hubs (Created page with "{{Needs Expansion}} Hugging Face")
• 12:47, 21 February 2023 Alpha5 talk contribs created page Model hub (Redirected page to Model hubs) Tag: New redirect
• 07:17, 21 February 2023 Alpha5 talk contribs created page Data sets (Redirected page to Datasets) Tag: New redirect
• 07:10, 21 February 2023 Alpha5 talk contribs created page Data set (Redirected page to Datasets) Tag: New redirect
• 07:09, 21 February 2023 Alpha5 talk contribs created page Dataset (Redirected page to Datasets) Tag: New redirect
• 07:09, 21 February 2023 Alpha5 talk contribs moved page Data set or dataset to Datasets
• 07:09, 21 February 2023 Alpha5 talk contribs created page Data set or dataset (Created page with "===Definition== Datasets in machine learning refer to a collection of information collected for training, testing, and assessing a model. They typically consist of input data (features) and their corresponding output or label data. Datasets can vary in size, format, and complexity depending on the problem being addressed. ==Importance== Datasets are essential elements in machine learning, as they serve to train, test and evaluate models. The quality and quantity of the...")
• 06:44, 21 February 2023 Alpha5 talk contribs created page Examples (Redirected page to Example) Tag: New redirect
• 06:44, 21 February 2023 Alpha5 talk contribs created page Features (Redirected page to Feature) Tag: New redirect
• 06:21, 21 February 2023 Alpha5 talk contribs created page DataFrame (Created page with "===Introduction== Data is the backbone of machine learning models. To effectively work with data, it must be organized and formatted for analysis - which is where DataFrames come into play. A DataFrame is a two-dimensional table-like data structure where rows and columns of information are organized. It's an essential concept in data analysis and widely employed in machine learning applications. ==Definition== DataFrame is a tabular data structure in which information i...")
• 16:51, 20 February 2023 Alpha5 talk contribs created page Convergence (Created page with "==Introduction== Machine learning aims to train a model to perform a specific task, such as recognizing images or predicting stock prices. The training process involves altering parameters in the model based on input data in order to minimize some objective function such as mean squared error between predicted and actual outputs. Convergence refers to when model parameters stop changing or do so slowly after being trained. ==Defining Convergence== Convergence is typical...")
• 14:49, 20 February 2023 Alpha5 talk contribs created page Clipping (Created page with "===Introduction== Clipping is a technique employed in machine learning to prevent the weights of a neural network from growing too large during optimization. Excess weights can lead to instability during training, causing the network to diverge and fail to converge on an optimal solution. ==The Need for Clipping== Machine learning algorithms like stochastic gradient descent (SGD) are commonly employed to update the weights of a neural network during training. SGD works...")
• 14:35, 20 February 2023 Alpha5 talk contribs created page Class imbalanced dataset (Redirected page to Class-imbalanced dataset) Tag: New redirect
• 14:31, 20 February 2023 Alpha5 talk contribs created page Class imbalance (Redirected page to Class-imbalance dataset) Tag: New redirect
• 14:30, 20 February 2023 Alpha5 talk contribs created page Class-imbalanced dataset (Created page with "===Introduction== Class imbalance is a frequent issue in machine learning, where one or more classes in a dataset have significantly fewer examples than others. This imbalance makes it difficult for machine learning algorithms to accurately predict the minority class, leading to biased and inaccurate models. This article will define class-imbalanced datasets in machine learning, discuss its challenges, and present various techniques to address this issue. ==Defining a...")
• 13:15, 20 February 2023 Alpha5 talk contribs created page Feature set (Created page with "==Introduction== In machine learning, a feature set is a collection of features or attributes that are used to represent a data point. These features are used to train a model to learn patterns in the data and make predictions on new data. The quality and relevance of the feature set are crucial to the success of the model, and feature engineering is an important step in the machine learning pipeline. ==What are features?== Features are properties or characteristics of...")
• 13:05, 20 February 2023 Alpha5 talk contribs deleted page Feature extraction (content was: "#REDIRECT Feature engineering", and the only contributor was "Alpha5" (talk))
• 12:59, 20 February 2023 Alpha5 talk contribs created page Feature extraction (Redirected page to Feature engineering) Tag: New redirect
• 12:54, 20 February 2023 Alpha5 talk contribs created page Feature engineering (Created page with "==Introduction== Feature engineering is a critical process in machine learning that involves selecting, extracting, and transforming relevant features or variables from raw data to improve the accuracy and performance of machine learning models. Feature engineering is a complex and challenging process that requires domain knowledge, creativity, and expertise in data manipulation techniques. The objective of feature engineering is to transform raw data into a more suitabl...")
• 12:27, 20 February 2023 Alpha5 talk contribs created page Machine learning models (Redirected page to Models) Tag: New redirect
• 12:26, 20 February 2023 Alpha5 talk contribs created page Categorical (Redirected page to Categorical data) Tag: New redirect
• 12:24, 20 February 2023 Alpha5 talk contribs created page Continuous (Redirected page to Continuous feature) Tag: New redirect
• 12:21, 20 February 2023 Alpha5 talk contribs created page Continuous feature (Created page with "===Introduction== Machine learning usually divides data into two primary types: continuous and categorical. Continuous features, also referred to as numerical or quantitative features, refer to variables that take on a range of numeric values like age, weight, and height. These features are commonly employed in regression models that aim to predict an output variable such as sales or revenue based on input features. Understanding continuous features is critical for creat...")
• 11:43, 20 February 2023 Alpha5 talk contribs created page Classification threshold (Created page with "===Introduction== Machine learning classification is a task where the goal is to assign an input data point to one of several predefined categories or classes. One critical decision that must be made while performing classification is setting the classification threshold; this determines when the algorithm assigns a data point to one class or another. ==What is Classification Threshold?== Classification threshold is a value that indicates the minimum probability that da...")
• 11:23, 20 February 2023 Alpha5 talk contribs created page Regression (Redirected page to Regression model) Tag: New redirect