User contributions for Thakshashila

10 June 2025

• 06:2306:23, 10 June 2025 diff hist +37 Micro F1 Score →SEO Keywords current
• 06:2206:22, 10 June 2025 diff hist +37 Macro F1 →SEO Keywords current
• 06:2106:21, 10 June 2025 diff hist +37 Artificial Intelligence AI →Conclusion current
• 06:2006:20, 10 June 2025 diff hist +37 Bias-Variance Tradeoff →SEO Keywords current
• 06:2006:20, 10 June 2025 diff hist +37 Classification →SEO Keywords current
• 06:2006:20, 10 June 2025 diff hist +37 Clustering →SEO Keywords current
• 06:2006:20, 10 June 2025 diff hist +37 Complementary metrics →SEO Keywords current
• 06:2006:20, 10 June 2025 diff hist +37 Confusion Matrix →SEO Keywords current
• 06:2006:20, 10 June 2025 diff hist +37 Cost-Sensitive Learning →SEO Keywords current
• 06:2006:20, 10 June 2025 diff hist +37 Cross Validation →SEO Keywords current
• 06:2006:20, 10 June 2025 diff hist +37 Dimensionality Reduction →SEO Keywords current
• 06:2006:20, 10 June 2025 diff hist +37 Evaluation Metrics →SEO Keywords current
• 06:2006:20, 10 June 2025 diff hist +37 F1 Score →SEO Keywords current
• 06:2006:20, 10 June 2025 diff hist +37 Hyperparameter Tuning →SEO Keywords current
• 06:2006:20, 10 June 2025 diff hist +37 Imbalanced Data →SEO Keywords current
• 06:1906:19, 10 June 2025 diff hist +37 Area Under Precision-Recall Curve (AUPRC) →SEO Keywords current
• 06:1906:19, 10 June 2025 diff hist +37 Accuracy →SEO Keywords
• 06:1506:15, 10 June 2025 diff hist +330 N Category:Artificial Intelligence Created page with "= Artificial Intelligence = This category includes all pages related to Artificial Intelligence (AI), including machine learning, deep learning, neural networks, and other AI-related techniques and applications. == Related Categories == * Category:Machine Learning * Category:Data Science * Category:Computer Science" current
• 06:1306:13, 10 June 2025 diff hist +37 AUC Score →SEO Keywords current
• 06:1106:11, 10 June 2025 diff hist +2,376 N Dimensionality Reduction Created page with "= Dimensionality Reduction = '''Dimensionality Reduction''' is a technique in machine learning and data analysis used to reduce the number of input variables (features) while preserving as much relevant information as possible. == Why Use Dimensionality Reduction? == High-dimensional data can lead to problems such as: * '''Overfitting:''' Too many features can cause the model to learn noise. * '''Increased Computation:''' More features = more time and resources...."
• 06:1006:10, 10 June 2025 diff hist +1,933 N Unsupervised Learning Created page with "= Unsupervised Learning = '''Unsupervised Learning''' is a type of machine learning where the model learns patterns and structures from unlabeled data without predefined outputs. == What is Unsupervised Learning? == In unsupervised learning, the input data has no associated labels. The goal is to explore the data’s inherent structure, group similar data points, or reduce the data’s dimensionality. == Common Types of Unsupervised Learning == * '''Clustering:''' G..."
• 06:0806:08, 10 June 2025 diff hist +2,174 N Clustering Created page with "= Clustering = '''Clustering''' is an unsupervised machine learning technique that groups data points into clusters such that points in the same cluster are more similar to each other than to those in other clusters. == What is Clustering? == Unlike supervised learning, clustering does not use labeled data. The goal is to find natural groupings or patterns within the data based on similarity or distance measures. == Types of Clustering == * '''Partitioning Methods:'..."
• 06:0506:05, 10 June 2025 diff hist +2,165 N Regression Created page with "= Regression = '''Regression''' is a type of supervised learning used to predict a continuous output variable based on one or more input features. == What is Regression? == In regression tasks, the goal is to model the relationship between input variables (features) and a continuous target variable. The model learns to estimate the output value for new inputs. == Types of Regression == * '''Simple Linear Regression:''' Models the relationship between a single input..."
• 06:0406:04, 10 June 2025 diff hist +2,130 N Supervised Learning Created page with "= Supervised Learning = '''Supervised Learning''' is a type of machine learning where the model learns to map input data to output labels using a labeled dataset. == What is Supervised Learning? == In supervised learning, each training example includes both the input features and the corresponding correct output (label). The goal is for the model to learn the relationship between inputs and outputs so it can predict the labels for new, unseen data. == Types of Superv..."
• 06:0006:00, 10 June 2025 diff hist +2,443 N Hyperparameter Tuning Created page with "= Hyperparameter Tuning = '''Hyperparameter Tuning''' is the process of optimizing the hyperparameters of a machine learning model to improve its performance on a specific task. == What are Hyperparameters? == Hyperparameters are settings or configurations external to the model that control the learning process. They are not learned from the data but set before training. Examples of hyperparameters include: * Learning rate in neural networks * Number of trees in a..."
• 05:5905:59, 10 June 2025 diff hist +2,621 N Bias-Variance Tradeoff Created page with "= Bias-Variance Tradeoff = '''Bias-Variance Tradeoff''' is a fundamental concept in machine learning that describes the balance between two sources of error that affect model performance: bias and variance. == What is Bias? == Bias refers to the error introduced by approximating a real-world problem, which may be complex, by a simpler model. A model with high bias pays little attention to the training data and oversimplifies the problem. * High bias can cause '''unde..."
• 05:5805:58, 10 June 2025 diff hist +2,403 N Regularization Created page with "= Regularization = '''Regularization''' is a technique in machine learning used to prevent '''overfitting''' by adding extra constraints or penalties to a model during training. == Why Regularization is Important == Overfitting happens when a model learns noise and details from the training data, harming its ability to generalize on new data. Regularization discourages overly complex models by penalizing large or unnecessary model parameters. == Common Types of Regul..."
• 05:5605:56, 10 June 2025 diff hist +2,279 N Train-Test Split Created page with "= Train-Test Split = '''Train-Test Split''' is a fundamental technique in machine learning used to evaluate the performance of a model by dividing the dataset into two parts: a training set and a testing set. == What is Train-Test Split? == The dataset is split into: * '''Training Set:''' Used to train the machine learning model. * '''Testing Set:''' Used to evaluate how well the trained model performs on unseen data. This helps measure the model’s ability to ge..."
• 05:5405:54, 10 June 2025 diff hist 0 Model Selection →Related Pages
• 05:5305:53, 10 June 2025 diff hist +1,900 N Underfitting Created page with "= Underfitting = '''Underfitting''' occurs when a machine learning model is too simple to capture the underlying pattern in the data, resulting in poor performance on both training and unseen data. == What is Underfitting? == Underfitting means the model fails to learn enough from the training data. It shows high errors during training and testing because it cannot capture important trends. == Causes of Underfitting == * '''Model Too Simple:''' Using a linear model..."
• 05:5105:51, 10 June 2025 diff hist 0 Overfitting →Related Pages
• 05:5105:51, 10 June 2025 diff hist +2,478 N Overfitting Created page with "= Overfitting = '''Overfitting''' is a common problem in machine learning where a model learns the training data too well, including its noise and outliers, resulting in poor performance on new, unseen data. == What is Overfitting? == When a model is overfitted, it captures not only the underlying pattern but also the random fluctuations or noise in the training dataset. This causes the model to perform excellently on training data but badly on test or real-world data..."
• 05:4905:49, 10 June 2025 diff hist −6 Weighted F1 →Related Pages
• 05:4905:49, 10 June 2025 diff hist −11 Micro F1 Score →Related Pages
• 05:4805:48, 10 June 2025 diff hist +5 Macro F1 →Related Pages
• 05:4805:48, 10 June 2025 diff hist −10 Macro F1 →Related Pages
• 05:4605:46, 10 June 2025 diff hist +3,086 N Evaluation Metrics Created page with "= Evaluation Metrics = '''Evaluation Metrics''' are quantitative measures used to assess the performance of machine learning models. Choosing the right metric is essential for understanding how well a model performs, especially in classification and regression problems. == Why Are Evaluation Metrics Important? == * Provide objective criteria to compare different models. * Help detect issues like overfitting or underfitting. * Guide model improvement and selection. * R..."
• 05:4505:45, 10 June 2025 diff hist +2,692 N Cost-Sensitive Learning Created page with "= Cost-Sensitive Learning = '''Cost-Sensitive Learning''' is a machine learning approach that incorporates different costs for different types of classification errors, helping models make better decisions in situations where misclassification errors have unequal consequences. == Why Cost-Sensitive Learning? == In many real-world problems, different mistakes have different costs. For example: * In medical diagnosis, a false negative (missing a disease) may be more co..."
• 05:4405:44, 10 June 2025 diff hist +2,294 N Area Under Precision-Recall Curve (AUPRC) Created page with "= Area Under Precision-Recall Curve (AUPRC) = The '''Area Under the Precision-Recall Curve''' ('''AUPRC''') is a single scalar value that summarizes the performance of a binary classification model by measuring the area under its Precision-Recall (PR) curve. == What is the Precision-Recall Curve? == The Precision-Recall Curve plots: * '''Precision''' (y-axis): the proportion of true positive predictions among all positive predictions. * '''Recall''' (x-axis): the pro..."
• 05:4005:40, 10 June 2025 diff hist +2,597 N Imbalanced Data Created page with "= Imbalanced Data = '''Imbalanced Data''' refers to datasets where the classes are not represented equally. In classification problems, one class (usually the positive or minority class) has far fewer examples than the other class (negative or majority class). == Why is Imbalanced Data a Problem? == Machine learning models often assume that classes are balanced and try to maximize overall accuracy. When data is imbalanced, models tend to be biased toward the majority..."
• 05:3605:36, 10 June 2025 diff hist +2,709 N Cross Validation Created page with "= Cross-Validation = '''Cross-Validation''' is a statistical method used to estimate the performance of machine learning models on unseen data. It helps ensure that the model generalizes well and reduces the risk of overfitting. == Why Cross-Validation? == When training a model, it is important to test how well it performs on data it has never seen before. Simply evaluating a model on the same data it was trained on can lead to overly optimistic results. Cross-validat..."
• 05:3505:35, 10 June 2025 diff hist +3,149 N Model Selection Created page with "= Model Selection = '''Model Selection''' is the process of choosing the best machine learning model from a set of candidate models based on their performance on a given task. It is a critical step to ensure the selected model generalizes well to new, unseen data. == Why Model Selection is Important == Different algorithms and model configurations may perform differently depending on the dataset and problem. Selecting the right model helps: * Improve prediction accur..."
• 05:3405:34, 10 June 2025 diff hist +2,615 N Threshold Tuning Created page with "= Threshold Tuning = '''Threshold Tuning''' is the process of selecting the best decision threshold in a classification model to optimize performance metrics such as Precision, Recall, F1 Score, or Accuracy. It is crucial in models that output '''probabilities''' rather than direct class labels. == Why Threshold Tuning Matters == Many classifiers (e.g., Logistic Regression, Neural Networks) output a probability score indicating how likely an instance b..."
• 05:3305:33, 10 June 2025 diff hist +2,652 N AUC Score Created page with "= AUC Score (Area Under the Curve) = The '''AUC Score''' refers to the '''Area Under the Curve''' and is a popular metric used to evaluate the performance of classification models, especially in binary classification tasks. Most commonly, AUC represents the area under the ROC Curve (Receiver Operating Characteristic Curve) or under the Precision-Recall Curve (PR Curve). == What is AUC? == AUC measures the ability of a model to distinguish between positive and..."
• 05:3205:32, 10 June 2025 diff hist +3,049 N Precision-Recall Curve Created page with "= Precision-Recall Curve = The '''Precision-Recall Curve''' (PR Curve) is a graphical representation used to evaluate the performance of binary classification models, especially on '''imbalanced datasets''' where the positive class is rare. It plots '''Precision''' (y-axis) against '''Recall''' (x-axis) for different classification thresholds. == Why Use Precision-Recall Curve? == In many real-world problems like fraud detection, disease diagnosis, or spam filtering,..."
• 05:3005:30, 10 June 2025 diff hist +3,373 N Model Evaluation Metrics Created page with "= Model Evaluation Metrics = '''Model Evaluation Metrics''' are quantitative measures used to assess how well a machine learning model performs. They help determine the accuracy, reliability, and usefulness of models in solving real-world problems. == Importance of Evaluation Metrics == Without evaluation metrics, it's impossible to know whether a model is effective or not. Metrics guide model selection, tuning, and deployment by measuring: * Accuracy of predictions..."
• 05:2905:29, 10 June 2025 diff hist +3,093 N Classification Created page with "= Classification = '''Classification''' is a fundamental task in '''machine learning''' and '''data science''' where the goal is to predict discrete labels (categories) for given input data. It is a type of '''supervised learning''' since the model learns from labeled examples. == What is Classification? == In classification, a model is trained on a dataset with input features and known target classes. Once trained, the model can assign class labels to new, unseen dat..."
• 05:2805:28, 10 June 2025 diff hist +2 ROC Curve →Ideal ROC Curve
• 05:2805:28, 10 June 2025 diff hist +2 ROC Curve →Limitations
• 05:2705:27, 10 June 2025 diff hist +2,637 N ROC Curve Created page with "= ROC Curve = The '''ROC Curve''' ('''Receiver Operating Characteristic Curve''') is a graphical tool used to evaluate the performance of binary classification models. It plots the '''True Positive Rate (TPR)''' against the '''False Positive Rate (FPR)''' at various threshold settings. == Purpose == The ROC Curve shows the trade-off between sensitivity (recall) and specificity. It helps assess how well a classifier can distinguish between two classes. == Definitions..."