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• 06:23, 23 February 2023 Alpha5 talk contribs created page File:Mnist example1.png (File uploaded with MsUpload)
• 06:23, 23 February 2023 Alpha5 talk contribs uploaded File:Mnist example1.png (File uploaded with MsUpload)
• 03:42, 23 February 2023 Alpha5 talk contribs created page Feature vector (Created page with "{{see also|Machine learning terms}} ===Introduction== Machine learning utilizes feature vectors, which are numerical values that describe an object or phenomenon. A feature vector may be defined as an n-dimensional array of numerical features representing a data point or example. As an array of feature values that represent an example, feature vector is used in training the model and using the model to make predictions (inferen...") Tag: Visual edit: Switched
• 21:59, 22 February 2023 Alpha5 talk contribs created page Sensitivity (Redirected page to True positive rate (TPR)) Tag: New redirect
• 20:58, 22 February 2023 Alpha5 talk contribs created page Z-score normalization (Created page with "{{see also|Machine learning terms}} ===Introduction== Data normalization in machine learning is a critical preprocessing step that helps boost the performance of many algorithms. Normalization involves scaling data to a specified range or distribution to reduce the impact of differences in scale or units of features. One widely-used technique for normalization is Z-score normalization (also referred to as standardization). ==What is Z-score normalization?== Z-score norm...")
• 15:37, 22 February 2023 Alpha5 talk contribs created page Weighted sum (Created page with "{{see also|Machine learning terms}} ===Introduction== In machine learning, a weighted sum is an algorithmic mathematical operation used to combine multiple input values by assigning weights to each. It's fundamental in many machine learning algorithms such as linear regression, neural networks and decision trees; this transformation transforms input data into one single output value which can then be used for prediction or classification purposes. ==Definition of Weight...")
• 15:27, 22 February 2023 Alpha5 talk contribs created page Weights (Redirected page to Weight) Tag: New redirect
• 14:41, 22 February 2023 Alpha5 talk contribs created page Weight (Created page with "{{see also|Machine learning terms}} ===Introduction== Machine learning uses weight as a fundamental concept to represent the strength of connections between nodes in a neural network. These connections form the basis for models' capacity to make accurate predictions and classifications by learning patterns from data. Understanding how weights are allocated and adjusted is essential in comprehending how a neural network functions. ==What is weight?== Machine learning ass...")
• 14:18, 22 February 2023 Alpha5 talk contribs created page Validation (Created page with "{{see also|Machine learning terms}} ===Introduction== Machine learning practitioners understand the importance of validation as one of the key steps in developing a predictive model. Validation measures the accuracy and dependability of a trained model by applying it to new data sets, with an aim of estimating its likely performance when applied. ==Training and Testing Data== Validating a machine learning model requires labeled data that can be used for training and tes...")
• 13:50, 22 February 2023 Alpha5 talk contribs created page Validation data set (Redirected page to Validation set) Tag: New redirect
• 13:49, 22 February 2023 Alpha5 talk contribs created page Validation data (Redirected page to Validation set) Tag: New redirect
• 13:49, 22 February 2023 Alpha5 talk contribs created page Validation set (Created page with "{{see also|Machine learning terms}} ===Introduction== Machine learning aims to construct predictive models that can accurately forecast new and unseen data. Training a machine learning model involves teaching it labeled data so it can learn patterns and relationships within it; however, after training the model, evaluation of its performance on unlabeled datasets must take place - this is where validation sets come into play. ==What is a validation set?== Validation set...")
• 13:28, 22 February 2023 Alpha5 talk contribs created page Validation loss (Created page with "{{see also|Machine learning terms}} ===Introduction== Validation loss in machine learning is a widely used metric to gauge the performance of models. It measures how well they can generalize their predictions to new data sets. In this article, we'll define validation loss and discuss its application to evaluating machine learning models. ==What is Validation Loss?== Validation loss is a metric that measures the performance of a machine learning model on a validation set...")
• 13:23, 22 February 2023 Alpha5 talk contribs created page Supervised learning (Redirected page to Supervised machine learning) Tag: New redirect
• 13:22, 22 February 2023 Alpha5 talk contribs created page Supervised training (Redirected page to Supervised machine learning) Tag: New redirect
• 13:13, 22 February 2023 Alpha5 talk contribs created page Unsupervised machine learning (Created page with "{{see also|Machine learning terms}} ===Introduction== Machine learning is a subset of artificial intelligence (AI), which allows computer programs to learn from data without being explicitly programmed. Machine learning models are trained using labeled data - that is, data that has been classified or labeled according to certain criteria. Unfortunately, not all data has been labeled and sometimes it's impossible to manually label it; in such cases unsupervised machine le...")
• 13:12, 22 February 2023 Alpha5 talk contribs created page Unsupervised learning (Redirected page to Unsupervised machine learning) Tag: New redirect
• 13:12, 22 February 2023 Alpha5 talk contribs created page Unsupervised (Redirected page to Unsupervised machine learning) Tag: New redirect
• 13:12, 22 February 2023 Alpha5 talk contribs created page Unsupervised training (Redirected page to Unsupervised machine learning) Tag: New redirect
• 12:59, 22 February 2023 Alpha5 talk contribs created page Labeled data (Redirected page to Labeled example) Tag: New redirect
• 12:58, 22 February 2023 Alpha5 talk contribs created page Unlabeled data (Redirected page to Unlabeled example) Tag: New redirect
• 12:56, 22 February 2023 Alpha5 talk contribs created page Unlabeled example (Created page with "{{see also|Machine learning terms}} ===Introduction== Machine learning often uses labeled data for model training. Labeled data refers to information that has already been classified and labeled by humans, making it simpler for the model to comprehend and learn from. On occasion, however, unlabeled data may also be employed. ==What is Unlabeled Data?== Unlabeled data, as its name suggests, refers to data that has not been labeled or categorized in any way. It's essentia...")
• 12:55, 22 February 2023 Alpha5 talk contribs created page Underfitted (Redirected page to Underfitting) Tag: New redirect
• 12:43, 22 February 2023 Alpha5 talk contribs created page Underfitting (Created page with "{{see also|Machine learning terms}} ===Introduction== Machine learning seeks to build models that can accurately predict the outcomes of unseen data based on patterns learned from training data. Unfortunately, developing an effective model is no small feat and many challenges arise along the way; one major issue being underfitting. Underfitting occurs when a model is too simple to capture underlying patterns in data. ==What is Underfitting?== Underfitting occurs when a...")
• 12:42, 22 February 2023 Alpha5 talk contribs created page Underfit (Redirected page to Underfitting) Tag: New redirect
• 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...")