automl/auto-sklearn

Features

• Automated Classification - Provides automated discovery and fitting of the best classification models and hyperparameters within resource constraints.
• Distributed Training - Scales the automated model search and optimization process across multiple CPU cores or clusters using Dask.
• Hyperparameter Optimization Tools - Provides a framework for tuning model settings using advanced techniques like Bayesian optimization and successive halving.
• Automated ML (AutoML) - Optimizes model selection and hyperparameters specifically for scikit-learn pipelines to automate the ML workflow.
• Data Preprocessing - Implements automated cleaning, normalization, and transformation of diverse data types to prepare them for machine learning models.
• Hyperparameter Optimization - Employs automated methods to search for and select the optimal configuration parameters for machine learning models.
• Automated - Automatically searches for and selects the most effective machine learning algorithms and configurations for a task.
• Automated Regression - Automatically finds and fits the best machine learning regression models and hyperparameters.
• Parallel Processing - Distributes automated searches and ensemble building across a cluster using Dask for large-scale parallel processing.
• Distributed Optimization Synchronization - Coordinates parallel model search trials across multiple processes or nodes to accelerate the discovery of optimal configurations.
• Parallel - Utilizes Dask to distribute model optimization trials across multiple CPU cores or clusters.
• Budget-Based Evaluation Strategies - Implements a budget-based evaluation strategy through successive halving to prioritize promising model configurations.
• Ensemble Methods - Automatically builds ensembles by aggregating high-performing models to improve overall predictive performance.
• Machine Learning Training - Allows customization of training through specific metrics, train-validation splits, and resampling strategies.
• Model Ensembling - Combines multiple high-performing models into a single ensemble to maximize predictive accuracy.
• Multi-Target Learning - Implements pipelines capable of predicting multiple continuous target variables simultaneously.
• Preprocessing Pipelines - Allows customization or disabling of the automatic preprocessing pipeline to control data transformations.
• Text Preprocessing Pipelines - Incorporates text-based data processing within the automated machine learning pipeline.
• Sparse Data Structures - Utilizes sparse matrices for input features and target labels to optimize memory for high-dimensional data.
• Multi-Objective Optimization - Balances multiple performance metrics simultaneously to find an optimal trade-off during model selection.
• Algorithmic Resource Limiters - Caps memory and disk space usage during model searches to prevent system crashes and filter inefficient configurations.
• Automated Machine Learning - Automated toolkit for model selection and hyperparameter tuning.
• AutoML - Automated machine learning toolkit for scikit-learn.
• Automated Machine Learning - Automated algorithm and hyperparameter tuning for scikit-learn pipelines.

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