online-ml/river

Features

• Incremental Model Training - Implements incremental learning by updating model parameters one observation at a time to eliminate the need for storing full training datasets.
• Online Learning - Provides a framework for training and updating machine learning models on streaming data one observation at a time.
• Incremental Model Updating - Updates model weights one observation at a time to enable learning without storing full datasets in memory.
• Incremental Training - Fits models to data streams by processing observations one by one to eliminate the need for full dataset storage.
• Model Performance Evaluators - Performs progressive validation to test model performance on a stream by comparing predictions against ground truth labels.
• Model Pipelines - Merges multiple preprocessing and modeling steps into a single sequence to process data streams.
• Progressive Stream Validators - Simulates real-time deployment by testing each observation before using it for model training.
• Stochastic Gradient Optimizers - Updates model weights using stochastic optimization algorithms such as Adam, SGD, and RMSProp.
• Streaming Feature Engineering - Converts raw streaming data into meaningful representations using categorical encoding and interaction analysis.
• Time Series Forecasting - Predicts future values in sequential data streams by learning from historical observations in real time.
• Drift Detection - Identifies changes in the underlying distribution of a data stream to trigger model retraining or alerts.
• Input Distribution Drift - Includes a dedicated system for monitoring input distribution drift to trigger model adaptation or alerts.
• Streaming Preprocessing Pipelines - Chains preprocessing and estimation steps into sequential workflows for transforming raw streaming features.
• Distribution Monitoring - Detects concept drift by comparing data distributions between recent and historical sliding windows.
• Online Running Statistics - Offers utilities for calculating running statistics and memory-efficient data sketches under fixed memory constraints.
• Streaming Data Sketches - Maintains memory-efficient sketches of data streams to track unique elements, heavy hitters, and histograms.
• Progressive Model Evaluation - Tests model accuracy on a stream using progressive validation to simulate real-world deployment.
• Anomaly Detection - Provides algorithms and tools for identifying outliers or unusual patterns in data streams.
• Filter-Based Feature Selection - Implements filter-based feature selection using statistical tests and variance thresholds to improve model efficiency on streaming data.
• Streaming Bandit Selectors - Implements bandit policies to balance exploration and exploitation for selecting the best model or action in streaming scenarios.
• Imbalanced Stream Resampling - Adjusts class distributions of incoming streaming data through resampling to prevent bias in imbalanced datasets.
• Incremental Decision Tree Learners - Grows decision trees incrementally using the Hoeffding bound to ensure statistical significance of splits.
• Ensemble Learning - Implements ensemble methods like bagging and boosting to improve predictive performance and robustness on streaming data.
• Model Ensembling - Combines multiple incremental models through bagging, boosting, or stacking to improve overall predictive performance.
• Stochastic Gradient Descent - Optimizes model parameters using iterative updates based on the gradient of individual samples.
• Mini-Batch Processing - Handles small groups of observations using data frames to balance batch efficiency with online learning requirements.
• Anomaly Detection - Identifies unusual observations in live data streams by scoring samples based on evolving distributions.
• Feature Scaling and Transformations - Scales numeric values and encodes categories in real time to ensure data compatibility with algorithms.
• Streaming Data Sketches - Implements memory-efficient probabilistic structures to track statistics of high-volume data streams.
• Personalized Ranking Optimizers - Provides algorithms for maximizing the posterior probability of user preferences to sort items for recommendation tasks.
• Streaming Data Clustering - Groups incoming observations into clusters incrementally without storing the full dataset.
• General Machine Learning - Framework for general-purpose online machine learning.
• Incremental Learning - Online machine learning.
• Online Machine Learning - Library for online machine learning.

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