Point positioning over the Earth´s surface has become simpler after the advent of positioning systems using artificial satellites. Nowadays, the satellites constellations of GNSS are GPS and GLONASS, the most structured systems, however, other systems were built to integrate the GNSS in last years. There are different methods to perform precise positioning using the data transmitted by GNSS satellites and the PPP method is one of these. Similarly to others, the PPP uses the observables to produce the coordinates and precise them. As we know, precision is different from accuracy. While precision informs the data set quality, accuracy tells us how much the coordinate is close to its real position on the ground. Although the correlation between precision and accuracy correlation is implicit in the observables, the processing methods cannot achieve it. The purpose of this study was to identify this relationship using the data mining tool known as Decision Tree. The creation of a large set of coordinates with known precision and accuracy were necessary for the recursive training of the Decision Tree, which became able to predict the coordinates' accuracy
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