Understanding from Machine Learning Models

UNDERSTANDING MACHINE LEARNING From Theory to Algorithms

Dust 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...

Machine 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...

Debugging Machine Learning Models

Creating a machine learning solution for a real world problem often becomes an iterative process of training, evaluation and improvement where the best practices and generic solutions are few and far between. Our work presents a novel solution for an essential step of this cycle: the process of understanding the root causes of ’bugs’ (particularly consequential or confusing test errors) discove...

Understanding Signal Sequences with Machine Learning

Protein translocation, the transport of newly synthesized proteins out of the cell, is a fundamental mechanism of life. We are interested in understanding how cells recognize the proteins that are to be exported and how the necessary information is encoded in the so called “Signal Sequences”. In this paper, we address these problems by building a physico-chemical model of signal sequence recogn...