Nouvelles stratégies pour la modélisation LOCAL

Interesting lecture from Pierre Dardenne: 

 

FOSS CALIBRATOR TIPS (SPLITING RULE)

 When we import a "cal" file into Foss Calibrator, we have the option to split this file into a training and validation set deciding the percentage of samples which goes to the training (80 by default) and to the validation set (20 by default), and the way this percentage is used (randomly), preserving the same distribution for every parameter, or based on time (the last 20% goes to validation and the older rest samples goes for training).

We can, anyway to import the "cal" file as a Validation set (will be used in the models as validation), as Training (will be used as training set for calibration) or None, being this last one important to hide, in same way, this set to the development of the model and change it later to Validation to check the performance for this particular set.

Selecting between ANN, MPLS or LOCAL calibrations

 

What is the best algorithm to analyze pH in soil. I try with MPLS, ANN and LOCAL. Models had been developed with a training set and we check the performance with a test set.

We can see that the performance is almost similar for ANN and LOCAL vs. the MPLS model.

LOCAL models have the advantage that we get the GH and NH values, so we can recalculate removing the high GHs values, that will be marked in red if the test samples would be analyzed in routine.

Sorry for some black cuts in the video.

SPAIN COVID-19 reports in "R"

 Interesting webpage where we can follow the evolution of the COVID-19 in Spain, and it is developed with R, so I recommend to all R users to consult it.

Link added on left side of the blog.

Please take care and keep safe.

Monitor function: Improved boxplot distribution

 Adding the "edaplot" function to the  predicted and reference values, we can get a better idea of the distributions and help to a better understanding about how the model work.  So this option is used to update the monitor boxplot function.