Prediction
Prediction is the output of a trained model when given new input data. Machine learning predictions can be categorical (classification), numerical (regression), or generative (text, images).
Understanding Prediction
Prediction is the core output of a machine learning model, representing the model's estimate or forecast for a given input based on patterns learned during training. In supervised learning, predictions take the form of class labels for classification tasks or continuous values for regression problems. The quality of predictions is evaluated using metrics like accuracy, precision, recall, and mean squared error, depending on the task type. Real-world prediction applications span virtually every industry: weather forecasting, stock price estimation, disease diagnosis, customer churn prediction, and demand planning in supply chains. The prediction process involves feeding new data through the trained model's parameters, applying learned transformations, and producing output through the final layer. Ensemble methods like random forests and gradient boosting combine predictions from multiple models to achieve greater accuracy and robustness than any single model alone.
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.