title: The Interdecadal pacific oscillation and the Southern Oscillation Index: relative merits for anticipating inflows to the Upper Clutha lakes.

authors: Taylor, Malcolm

subjects: Southern Oscillation Index

abstract: Spring values of the Southern Oscillation Index (SOI) and the unfiltered Interdecadal Pacific Oscillation (IPO) are compared for anticipating summer inflows to the lakes of the upper Clutha River, New Zealand. Earlier work proposed the spring SOI to forecast summer inflows. However, more recent data, which includes a change of state of the IPO, indicate the spring SOI only has a degree of predictive ability for the low summer flows. Furthermore, this low-flow predictability requires La Niña conditions (spring SOI>5). The spring IPO index, in contrast, can anticipate both high and low summer inflows when the spring SOI >0, and thus has a wider range of applicability for summer inflow forecasting than the SOI. Both the IPO and SOI appear to have no summer inflow forecasting ability when the spring SOI is negative.

text: The Interdecadal pacific oscillation and the Southern Oscillation Index: relative merits for anticipating inflows to the Upper Clutha lakes. Spring values of the Southern Oscillation Index (SOI) and the unfiltered Interdecadal Pacific Oscillation (IPO) are compared for anticipating summer inflows to the lakes of the upper Clutha River, New Zealand. Earlier work proposed the spring SOI to forecast summer inflows. However, more recent data, which includes a change of state of the IPO, indicate the spring SOI only has a degree of predictive ability for the low summer flows. Furthermore, this low-flow predictability requires La Niña conditions (spring SOI>5). The spring IPO index, in contrast, can anticipate both high and low summer inflows when the spring SOI >0, and thus has a wider range of applicability for summer inflow forecasting than the SOI. Both the IPO and SOI appear to have no summer inflow forecasting ability when the spring SOI is negative.

year: 2015

title: Naive Bayes for regression

authors: Frank, Eibe

subjects: Science & Technology

abstract: Despite its simplicity, the naive Bayes learning scheme performs well on most classification tasks, and is often significantly more accurate than more sophisticated methods. Although the probability estimates that it produces can be inaccurate, it often assigns maximum probability to the correct class. This suggests that its good performance might be restricted to situations where the output is categorical. It is therefore interesting to see how it performs in domains where the predicted value is numeric, because in this case, predictions are more sensitive to inaccurate probability estimates.
This paper shows how to apply the naive Bayes methodology to numeric prediction (i.e., regression) tasks by modeling the probability distribution of the target value with kernel density estimators, and compares it to linear regression, locally weighted linear regression, and a method that produces “model trees”—decision trees with linear regression functions at the leaves. Although we exhibit an artificial dataset for which naive Bayes is the method of choice, on real-world datasets it is almost uniformly worse than locally weighted linear regression and model trees. The comparison with linear regression depends on the error measure: for one measure naive Bayes performs similarly, while for another it is worse. We also show that standard naive Bayes applied to regression problems by discretizing the target value performs similarly badly.We then present empirical evidence that isolates naive Bayes’ independence assumption as the culprit for its poor performance in the regression setting. These results indicate that the simplistic statistical assumption that naive Bayes makes is indeed more restrictive for regression than for classification.

text: Naive Bayes for regression Despite its simplicity, the naive Bayes learning scheme performs well on most classification tasks, and is often significantly more accurate than more sophisticated methods. Although the probability estimates that it produces can be inaccurate, it often assigns maximum probability to the correct class. This suggests that its good performance might be restricted to situations where the output is categorical. It is therefore interesting to see how it performs in domains where the predicted value is numeric, because in this case, predictions are more sensitive to inaccurate probability estimates.
This paper shows how to apply the naive Bayes methodology to numeric prediction (i.e., regression) tasks by modeling the probability distribution of the target value with kernel density estimators, and compares it to linear regression, locally weighted linear regression, and a method that produces “model trees”—decision trees with linear regression functions at the leaves. Although we exhibit an artificial dataset for which naive Bayes is the method of choice, on real-world datasets it is almost uniformly worse than locally weighted linear regression and model trees. The comparison with linear regression depends on the error measure: for one measure naive Bayes performs similarly, while for another it is worse. We also show that standard naive Bayes applied to regression problems by discretizing the target value performs similarly badly.We then present empirical evidence that isolates naive Bayes’ independence assumption as the culprit for its poor performance in the regression setting. These results indicate that the simplistic statistical assumption that naive Bayes makes is indeed more restrictive for regression than for classification.

year: 2000-10-01

title: Restorative Practices in Schools: Far-Reaching Implications

authors: Drewery, Wendy

subjects: New Zealand

abstract: In 1999, the New Zealand Ministry of Education contracted a team from the University of Waikato to develop a process for conferencing in schools. The brief was to utilise restorative justice principles to develop a conferencing process for use in schools. The purpose was to test whether such an approach to wrongdoing could reduce the exponential increase of suspensions, particularly of Maori boys, who were, and still are, disproportionately represented in reported numbers of suspensions, stand-downs and exclusions.

text: Restorative Practices in Schools: Far-Reaching Implications In 1999, the New Zealand Ministry of Education contracted a team from the University of Waikato to develop a process for conferencing in schools. The brief was to utilise restorative justice principles to develop a conferencing process for use in schools. The purpose was to test whether such an approach to wrongdoing could reduce the exponential increase of suspensions, particularly of Maori boys, who were, and still are, disproportionately represented in reported numbers of suspensions, stand-downs and exclusions.

year: 2007