Linear Trees combine the learning ability of Decision Tree with the predictive and explicative power of Linear Models. Like in tree-based algorithms, the data are split according ... Firstly, a Linear ...

general model, including all samples (n = 120, 90 for modeling and 30 for validation). In order to adjust the models for predicting soil property results from the pXRF data, two methods were tested: ...

He pointed out the importance of seamless data flow across borders which is imperative to spur innovation, job creation, regional integration and cohesion. Kabogo noted that as a region ...

diagrams to represent structural characteristics of data. However, ER diagrams lack the capability to capture the flow and transformation of data through analytic pipelines, which are essential to ...

This paper presents a sample-rebalanced and outlier-rejected k-nearest neighbor regression model for short-term traffic flow forecasting. In this model, we adopt a new metric for the evolutionary ...

Predicting car prices using multiple linear regression. This project uses real-world automotive data to train a machine learning model capable of estimating car prices based on technical ...