Gaussian Process
A Gaussian process is a probabilistic model that defines a distribution over functions, providing both predictions and uncertainty estimates. Gaussian processes are used in Bayesian optimization and surrogate modeling.
Understanding Gaussian Process
A Gaussian process is a probabilistic machine learning model that defines a distribution over functions, providing both predictions and uncertainty estimates for regression and classification tasks. Unlike neural networks that produce point estimates, Gaussian processes naturally quantify how confident they are in predictions, making them valuable in applications where understanding uncertainty is critical, such as Bayesian optimization for hyperparameter tuning, robotics, and scientific experimentation. A Gaussian process is fully specified by a mean function and a kernel function that encodes assumptions about data smoothness and structure. While powerful for small to medium datasets, Gaussian processes scale poorly to large datasets due to cubic computational complexity, prompting research into sparse approximations and deep kernel learning that combines them with deep learning. They connect to concepts in ensemble learning and reinforcement learning where uncertainty-driven exploration vs exploitation decisions are essential.
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.