The right of habitation is the one given by the law; it consists that, after the death of the testator, the surviving spouse or the surviving cohabitant who doesn’t have   enough resources is allowed to have the house-room gratuitously and for life.In this article, the author explains some differences and similarities of the Peruvian regulation of such right of habitation compared with those existing in other Civil Codes, its necessary requirements, the possible consequences it may have on the rest of the heirs and the situations in which this right canbe applied. / El derecho de habitación es aquel por el cualla ley permite que, ante la muerte del testador, el cónyuge supérstite o el sobrevivientede la unión de hecho, en caso de no contar conrecursos  suficientes,  puedan  adjudicarse  la casa-habitación de forma gratuita y vitalicia.En el presente artículo, el autor establece algunas diferencias y semejanzas de la regulación de dicha figura con relación a otros códigos civiles, los requisitos para que proceda dicho derecho, las posibles consecuencias que pueden tener en los demás herederos y los escenarios en que se puede presentar.

The decision tree is a well-known methodology for classification and regression. In this dissertation, we focus on the minimization of the misclassification rate for decision tree classifiers. We derive the necessary equations that provide the optimal tree prediction, the estimated risk of the tree's prediction, and the reliability of the tree's risk estimation. We carry out an extensive analysis of the application of Lidstone's law of succession for the estimation of the class probabilities. In contrast to existing research, we not only compute the expected values of the risks but also calculate the corresponding reliability of the risk (measured by standard deviations). We also provide an explicit expression of the k-norm estimation for the tree's misclassification rate that combines both the expected value and the reliability. Furthermore, our proposed and proven theorem on k-norm estimation suggests an efficient pruning algorithm that has a clear theoretical interpretation, is easily implemented, and does not require a validation set. Our experiments show that our proposed pruning algorithm produces accurate trees quickly that compares very favorably with two other well-known pruning algorithms, CCP of CART and EBP of C4.5. Finally, our work provides a deeper understanding of decision trees.