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Optimization of prediction approach for improving students orientation in university: Case of Sidi Mohammed Ben Abdelah University (SMBAU)

机译:优化预测方法以改善大学中的学生导向:以西迪·穆罕默德·本·阿卜杜拉大学(SMBAU)为例

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Determine the right size of the tree is a crucial operation in the construction of a decision tree from a large volume of data. It largely determines its performance when deployed in the population. This is to avoid two extremes: the sub-study, defined by a tree too small, poorly capturing useful information of the training data; overfitting, defined by a exaggerated size of tree, capturing the specific training data, characteristics that can not be transposed in the population. In both cases, we have a little performance prediction model. Our work is summarized by optimizing a predictive model with the CART method for pre-university orientation s[1]; This is a compromise between the reliability of the model on the training set and the generalization of the model case University Sidi Mohammed Ben Abdelah (SMBAU) of Fez.
机译:确定树的正确大小是从大量数据构建决策树的关键操作。它在人群中部署时很大程度上决定了其性能。这是为了避免两个极端:子树,树太小,无法很好地捕获训练数据的有用信息;过度拟合,即通过夸大的树的大小定义,捕获特定的训练数据以及无法在总体中转换的特征。在这两种情况下,我们都有一些性能预测模型。我们的工作通过针对大学预科方向s [1]的CART方法优化预测模型进行总结。这是训练集上模型的可靠性与非斯大学Sidi Mohammed Ben Abdelah(SMBAU)模型案例的泛化之间的折衷。

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