title: Logistic model trees

authors: Landwehr, Niels

subjects: Science & Technology

abstract: Tree induction methods and linear models are popular techniques for supervised learning tasks, both for the prediction of nominal classes and continuous numeric values. For predicting numeric quantities, there has been work on combining these two schemes into ‘model trees’, i.e. trees that contain linear regression functions at the leaves. In this paper, we present an algorithm that adapts this idea for classification problems, using logistic regression instead of linear regression. We use a stagewise fitting process to construct the logistic regression models that can select relevant attributes in the data in a natural way, and show how this approach can be used to build the logistic regression models at the leaves by incrementally refining those constructed at higher levels in the tree. We compare the performance of our algorithm against that of decision trees and logistic regression on 32 benchmark UCI datasets, and show that it achieves a higher classification accuracy on average than the other two methods.

text: Logistic model trees Tree induction methods and linear models are popular techniques for supervised learning tasks, both for the prediction of nominal classes and continuous numeric values. For predicting numeric quantities, there has been work on combining these two schemes into ‘model trees’, i.e. trees that contain linear regression functions at the leaves. In this paper, we present an algorithm that adapts this idea for classification problems, using logistic regression instead of linear regression. We use a stagewise fitting process to construct the logistic regression models that can select relevant attributes in the data in a natural way, and show how this approach can be used to build the logistic regression models at the leaves by incrementally refining those constructed at higher levels in the tree. We compare the performance of our algorithm against that of decision trees and logistic regression on 32 benchmark UCI datasets, and show that it achieves a higher classification accuracy on average than the other two methods.

year: 2003-01-01

title: Agglomeration elasticities and firm heterogeneity

authors: Maré, David C.

subjects: agglomeration

abstract: This paper examines three key issues encountered when estimating the relationship between agglomeration and multi factor productivity ('agglomeration elasticities'): the sorting of heterogeneous firms, the convexity of agglomeration effects, and the challenges of identifying the impact of persistent spatial differences in effective density. We use a firm-level panel containing production data together with detailed information on the geographic location of employment, covering a high proportion of the New Zealand economy. We are able to control for heterogeneity along firm, region, and industry dimensions, and to estimate separate agglomeration elasticities across industries and regions. Sorting leads to upward biased elasticity estimates but using firm fixed effects can lead to downward bias due to the highly persistent nature of agglomeration variables. Our preferred estimates control for sorting across regions and industries. Overall, we find a positive agglomeration elasticity of 0.066. Within industries and, to a lesser extent within regions, there is pronounced variation in the strength of agglomeration effects, and evidence of decreasing returns to agglomeration. High density areas attract firms that benefit most from agglomeration.

text: Agglomeration elasticities and firm heterogeneity This paper examines three key issues encountered when estimating the relationship between agglomeration and multi factor productivity ('agglomeration elasticities'): the sorting of heterogeneous firms, the convexity of agglomeration effects, and the challenges of identifying the impact of persistent spatial differences in effective density. We use a firm-level panel containing production data together with detailed information on the geographic location of employment, covering a high proportion of the New Zealand economy. We are able to control for heterogeneity along firm, region, and industry dimensions, and to estimate separate agglomeration elasticities across industries and regions. Sorting leads to upward biased elasticity estimates but using firm fixed effects can lead to downward bias due to the highly persistent nature of agglomeration variables. Our preferred estimates control for sorting across regions and industries. Overall, we find a positive agglomeration elasticity of 0.066. Within industries and, to a lesser extent within regions, there is pronounced variation in the strength of agglomeration effects, and evidence of decreasing returns to agglomeration. High density areas attract firms that benefit most from agglomeration.

year: 2013

title: Racism and bullying as correlates for considering ending psychology training in Aotearoa New Zealand

authors: Tan, Kyle K. H.

abstract: Despite global calls to enhance culturally safe care for Indigenous and minoritised groups, little attention has been given to exploring students’ experiences in psychology training programmes to meet these needs. A series of chi-square tests was performed to examine group differences for participants in the Kia Whakapapa Pounaumu survey (n = 107) in their consideration of ending their training and their decision to seek professional help. Over half (55%) of students reported having considered ending their psychology training. Our findings show that students who have experienced or witnessed forms of injustice such as institutional racism, microaggressions, and bullying, encounter additional barriers in completing their training. If psychology bodies wish to redress the harm caused to Indigenous and minoritised groups in the discipline of psychology, they must ensure the training programmes themselves do not perpetuate harm.

text: Racism and bullying as correlates for considering ending psychology training in Aotearoa New Zealand Despite global calls to enhance culturally safe care for Indigenous and minoritised groups, little attention has been given to exploring students’ experiences in psychology training programmes to meet these needs. A series of chi-square tests was performed to examine group differences for participants in the Kia Whakapapa Pounaumu survey (n = 107) in their consideration of ending their training and their decision to seek professional help. Over half (55%) of students reported having considered ending their psychology training. Our findings show that students who have experienced or witnessed forms of injustice such as institutional racism, microaggressions, and bullying, encounter additional barriers in completing their training. If psychology bodies wish to redress the harm caused to Indigenous and minoritised groups in the discipline of psychology, they must ensure the training programmes themselves do not perpetuate harm.

year: 2025