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Table 3 Temporal out-of-sample results of the regressors using different subsets of the features: trained on 2014 and tested on 2015

From: Mining large-scale human mobility data for long-term crime prediction

  Census Census + POI Human Dynamics Census + Human Dynamics
  MSE \(R^{2}\) MSE \(R^{2}\) MSE \(R^{2}\) MSE \(R^{2}\)
Total incidents         
Random Forest 0.11 0.82 0.07 0.88 0.09 0.84 0.07 0.88
Extra-Tree 0.11 0.82 0.07 0.89 0.08 0.87 0.07 0.89
Gradient Boosting 0.22 0.64 0.09 0.85 0.12 0.80 0.08 0.87
Grand larcenies         
Random Forest 0.19 0.73 0.14 0.81 0.14 0.81 0.13 0.82
Extra-Tree 0.21 0.71 0.14 0.81 0.14 0.80 0.14 0.80
Gradient Boosting 0.28 0.61 0.17 0.77 0.16 0.78 0.15 0.79
Robberies         
Random Forest 0.27 0.71 0.24 0.75 0.28 0.70 0.23 0.75
Extra-Tree 0.26 0.72 0.23 0.75 0.27 0.70 0.27 0.71
Gradient Boosting 0.38 0.59 0.29 0.69 0.32 0.66 0.28 0.70
Burglaries         
Random Forest 0.25 0.47 0.24 0.50 0.25 0.47 0.24 0.49
Extra-Tree 0.25 0.47 0.24 0.50 0.33 0.32 0.32 0.34
Gradient Boosting 0.30 0.38 0.25 0.47 0.27 0.42 0.23 0.51
Assaults         
Random Forest 0.24 0.75 0.22 0.77 0.28 0.72 0.22 0.77
Extra-Tree 0.24 0.76 0.22 0.78 0.28 0.71 0.27 0.73
Gradient Boosting 0.34 0.65 0.29 0.71 0.46 0.53 0.24 0.76
Vehicle larcenies         
Random Forest 0.31 0.31 0.29 0.34 0.31 0.31 0.30 0.34
Extra-Tree 0.33 0.27 0.29 0.36 0.37 0.16 0.38 0.15
Gradient Boosting 0.32 0.28 0.31 0.30 0.34 0.23 0.30 0.33