DriveML: An R Package for Driverless Machine Learning
In recent years, the concept of automated machine learning has become very popular. Automated Machine Learning (AutoML) mainly refers to the automated methods for model selection and hyper-parameter optimization of various algorithms such as random forests, gradient boosting, neural networks, etc. In this paper, we introduce a new package i.e. DriveML for automated machine learning. DriveML helps in implementing some of the pillars of an automated machine learning pipeline such as automated data preparation, feature engineering, model building and model explanation by running the function instead of writing lengthy R codes. The DriveML package is available in CRAN. We compare the DriveML package with other relevant packages in CRAN/Github and find that DriveML performs the best across different parameters. We also provide an illustration by applying the DriveML package with default configuration on a real world dataset. Overall, the main benefits of DriveML are in development time savings, reduce developer's errors, optimal tuning of machine learning models and reproducibility.
Code (1)
Tasks
AutoMLBIG-bench Machine LearningFeature EngineeringModel SelectionSimilar Papers 제목 키워드 기반
DriveMLLM: A Benchmark for Spatial Understanding with Multimodal Large Language Models in Autonomous Driving
Autonomous driving requires a comprehensive understanding of 3D environments to facilitate high-level tasks such as motion prediction, planning, and mapping. In this paper, we introduce DriveMLLM, a benchmark specificall…
Autonomous Drivingmotion predictionSpatial ReasoningDriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Driving
Large language models (LLMs) have opened up new possibilities for intelligent agents, endowing them with human-like thinking and cognitive abilities. In this work, we delve into the potential of large language models (LL…
Autonomous DrivingMotion PlanningTowards a safe MLOps Process for the Continuous Development and Safety Assurance of ML-based Systems in the Railway Domain
Traditional automation technologies alone are not sufficient to enable driverless operation of trains (called Grade of Automation (GoA) 4) on non-restricted infrastructure. The required perception tasks are nowadays real…
RailGoerl24: Görlitz Rail Test Center CV Dataset 2024
Driverless train operation for open tracks on urban guided transport and mainline railways requires, among other things automatic detection of actual and potential obstacles, especially humans, in the danger zone of the …
OSDaR23: Open Sensor Data for Rail 2023
To achieve a driverless train operation on mainline railways, actual and potential obstacles for the train's driveway must be detected automatically by appropriate sensor systems. Machine learning algorithms have proven …
object-detectionObject Detection