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Version of Record: https://www.sciencedirect.com/science/article/pii/S0198971522000989 Manuscript_ceed927488e61021d027535bbbaa764eA Machine Learning Approach to Evaluate the Spatial Variability of New York Citys 311 Street Flooding Complaints Candace Agonafirab*, Tarendra Lakhankarb, Reza Khanbilvardiab, Nir Krakauerab, Dave Radellc, Naresh Devineniab*a Dept. of Civil Engineering, The City University of New York (City College) New York, NY 10031, United States b National Oceanic and Atmospheric
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What is a machine learning approach?
A machine learning approach is a method that uses algorithms to analyze data, learn from it, and make predictions or decisions without being explicitly programmed for each task.
Who is required to file a machine learning approach?
Researchers, data scientists, and organizations that implement machine learning models, particularly in regulated industries such as finance, healthcare, and technology, are required to file a machine learning approach.
How to fill out a machine learning approach?
To fill out a machine learning approach, one needs to define the problem, select appropriate algorithms, gather and preprocess data, train the model, evaluate its performance, and document the methodologies and results.
What is the purpose of a machine learning approach?
The purpose of a machine learning approach is to automate data analysis, gain insights from large datasets, improve decision-making, and enhance predictive capabilities in various applications.
What information must be reported on a machine learning approach?
Information that must be reported includes the algorithms used, data sources, model performance metrics, validation methods, assumptions made, and any limitations or potential biases identified during the modeling process.
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