Decision Boundary
A decision boundary is the surface or line that separates different classes in a classification model's feature space. The shape and complexity of decision boundaries depend on the model architecture and training data.
Understanding Decision Boundary
A decision boundary is the surface or line in feature space that a classification model uses to separate data points belonging to different classes. Simple models like logistic regression produce linear decision boundaries, while more complex models such as support vector machines with kernel tricks, decision trees, and deep neural networks can learn highly non-linear boundaries. The shape and complexity of the decision boundary directly affect how well a model generalizes: overly complex boundaries may overfit training data, while overly simple ones may underfit. Visualizing decision boundaries in two or three dimensions is a common technique for understanding model behavior during evaluation. Regularization techniques like dropout and weight decay help control boundary complexity, striking a balance between fitting the training data and performing well on unseen examples.
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.