Label
A label is the target output or ground truth annotation associated with a training example in supervised learning. Models learn to predict correct labels from input features during the training process.
Understanding Label
A label is the target value or category assigned to a data point in supervised learning, representing the correct answer that a model learns to predict. Labels can be categorical (such as "cat" or "dog" in image classification), continuous (such as a house price in linear regression), or structured (such as bounding boxes in object detection). The process of creating labels, known as annotation, is often the most time-consuming and expensive part of building machine learning systems, frequently requiring human experts. Label quality directly impacts model performance, and noisy or incorrect labels can introduce bias and degrade accuracy. Semi-supervised learning and self-supervised learning are paradigms that reduce dependence on labeled data by leveraging large amounts of unlabeled examples. Active learning strategically selects the most informative samples for labeling to maximize efficiency.
Category
Machine Learning
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Accuracy
Accuracy is a metric that measures the proportion of correct predictions out of total predictions made by a model. While intuitive, accuracy can be misleading on imbalanced datasets where one class dominates.
Active Learning
Active learning is a machine learning approach where the model selectively queries an oracle (often a human) for labels on the most informative data points. This reduces the total amount of labeled data needed to train an accurate model.
Anomaly Detection
Anomaly detection is the identification of data points, events, or patterns that deviate significantly from expected behavior. AI-based anomaly detection is used in fraud prevention, cybersecurity, and industrial monitoring.
AutoML
Automated Machine Learning (AutoML) is the process of automating the end-to-end pipeline of applying machine learning, including feature engineering, model selection, and hyperparameter tuning. AutoML democratizes AI by reducing the expertise required.
Bagging
Bagging (Bootstrap Aggregating) is an ensemble technique that trains multiple models on random subsets of training data and combines their predictions. Random Forest is the most well-known bagging-based algorithm.
Bayesian Network
A Bayesian network is a probabilistic graphical model that represents variables and their conditional dependencies using a directed acyclic graph. It enables reasoning under uncertainty and causal inference.
Bias-Variance Tradeoff
The bias-variance tradeoff is the fundamental tension in machine learning between model simplicity (high bias) and model flexibility (high variance). Optimal models balance underfitting and overfitting to generalize well to new data.
Binary Classification
Binary classification is a supervised learning task where the model assigns inputs to one of exactly two categories. Spam detection (spam vs. not spam) and medical diagnosis (positive vs. negative) are common examples.