Model performance assessment is a key procedure for mineral potential mapping, but the correspond-ing research achievements are seldom reported in literature. Cumulative gain and lift charts are well known in the data...Model performance assessment is a key procedure for mineral potential mapping, but the correspond-ing research achievements are seldom reported in literature. Cumulative gain and lift charts are well known in the data mining community specialized in marketing and sales applications and widely used in customer chum prediction for model performance assessment. In this paper, they are introduced into the field of mineral poten-tial mapping for model performance assessment. These two charts can be viewed as a graphic representation of the advantage of using a predictive model to choose mineral targets. A cumulative gain curve can represent how much a predictive model is superior to a random guess in mineral target prediction. A lift chart can express how much more likely the mineral targets predicted by a model are deposit-bearing ones than those by a random se-lection. As an illustration, the cumulative gain and lift charts are applied to measure the performance of weights of evidence, logistic regression,restricted Boltzmann machine, and multilayer perceptron in mineral potential mapping in the Altay district in northern Xinjiang in China. The results show that the cumulative gain and lift charts can visually reveal that the first three models perform well while the last one performs poorly. Thus, the cumulative gain and lift charts can serve as a graphic tool for model performance assessment in mineral potential mapping.展开更多
基金Supported by Project of the National Natural Science Foundation of China(Nos.41272360,41472299,61133011)
文摘Model performance assessment is a key procedure for mineral potential mapping, but the correspond-ing research achievements are seldom reported in literature. Cumulative gain and lift charts are well known in the data mining community specialized in marketing and sales applications and widely used in customer chum prediction for model performance assessment. In this paper, they are introduced into the field of mineral poten-tial mapping for model performance assessment. These two charts can be viewed as a graphic representation of the advantage of using a predictive model to choose mineral targets. A cumulative gain curve can represent how much a predictive model is superior to a random guess in mineral target prediction. A lift chart can express how much more likely the mineral targets predicted by a model are deposit-bearing ones than those by a random se-lection. As an illustration, the cumulative gain and lift charts are applied to measure the performance of weights of evidence, logistic regression,restricted Boltzmann machine, and multilayer perceptron in mineral potential mapping in the Altay district in northern Xinjiang in China. The results show that the cumulative gain and lift charts can visually reveal that the first three models perform well while the last one performs poorly. Thus, the cumulative gain and lift charts can serve as a graphic tool for model performance assessment in mineral potential mapping.
