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archive3d
Introduction to machine learning with Python : a guide for data scientists
Ask the publishers to restore access to 500,000+ books. The Internet Archive keeps the record straight by preserving government websites, news publications, historical documents, and more. If you find ...
GitHub6d
A python library to build Model Trees with Linear Models at the leaves.
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 ... SciPy and ...
SciELO16d
Multiple linear regression and random forest to predict and map soil properties using data from portable X-ray fluorescence spectrometer (pXRF)
In order to adjust the models for predicting soil property results from the pXRF data, two methods were tested: stepwise multiple linear regression (SMLR) and random forest algorithm (RF). The SMLR ...
Carnegie Institution for Science17d
Fast and slow climate responses to CO2 and solar forcing: A linear multivariate regression model characterizing transient climate change
To achieve this goal, we develop a multivariate regression model of climate change, in which the change in a climate variable is represented by a linear combination of its sensitivity to CO2 forcing, ...
IEEE21d
Clustering Multivariate Time Series Data via Multi-Nonnegative Matrix Factorization in Multi-Relational Networks
Abstract: In multivariate time series clustering, the inter-similarity across distinct variates and the intra-similarity within each variate pose analytical challenges. Here, we propose a novel ...
Frontiers24d
Quantitative relationship model between soil profile salinity and soil depth in cotton fields based on data assimilation algorithm: forecasting cotton field yields and profits
The apparent conductivity at different effective depths was used to predict the measured conductivity at different periods and soil depths, and then a multiple linear regression ... to establish a ...
IEEE25d
Assessing Differences in Large Spatio-temporal Climate Datasets with a New Python package
our motivation is to enable climate scientists to more easily assess the effects of lossy data compression by visualizing and computing derived spatial-temporal quantities that compare lossily ...
Nieman Journalism Lab27d
How do you report on the weather when data is disappearing?
A reduced staff means fewer people collecting and analyzing the data we need to understand the weather and how climate change is impacting it. Historically, NWS offices across the country have ...