Reply: metrics to assess machine learning models
نویسندگان
چکیده
منابع مشابه
Models, Metrics, and an Index to Assess Humanitarian Response Capacity
The race to meet vital needs following sudden onset disasters leads response organizations to establish stockpiles of inventory that can be deployed immediately, without needing to rely on suppliers. These government or non-government organizations dynamically make stockpile decisions in isolation, and they do so without empirical metrics on how the stock will improve the response effort. To ad...
متن کاملDust source mapping using satellite imagery and machine learning models
Predicting dust sources area and determining the affecting factors is necessary in order to prioritize management and practice deal with desertification due to wind erosion in arid areas. Therefore, this study aimed to evaluate the application of three machine learning models (including generalized linear model, artificial neural network, random forest) to predict the vulnerability of dust cent...
متن کاملMachine Learning Models for Housing Prices Forecasting using Registration Data
This article has been compiled to identify the best model of housing price forecasting using machine learning methods with maximum accuracy and minimum error. Five important machine learning algorithms are used to predict housing prices, including Nearest Neighbor Regression Algorithm (KNNR), Support Vector Regression Algorithm (SVR), Random Forest Regression Algorithm (RFR), Extreme Gradient B...
متن کاملEmail Reply Prediction: A Machine Learning Approach
Email has now become the most-used communication tool in the world and has also become the primary business productivity applications for most organizations and individuals. With the ever increasing popularity of emails, email over-load and prioritization becomes a major problem for many email users. Users spend a lot of time reading, replying and organizing their emails. To help users organize...
متن کاملApplying Metrics to Machine-Learning Tools: A Knowledge Engineering Approach
The field of knowledge engineering has been one of the most visible successes of artificial intelligence (AI) to date. Knowledge acquisition (KA) is the main bottleneck in the knowledge engineer's (KE) work. Machine learning (ML) tools have contributed positively to the process of trying to eliminate or open up this bottleneck. But how do we know whether the field is progressing? How can we est...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: npj Digital Medicine
سال: 2018
ISSN: 2398-6352
DOI: 10.1038/s41746-018-0063-z