title: Naive Bayes for regression

authors: Frank, Eibe

subjects: computer science

abstract: Despite its simplicity, the naïve 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 naïve Bayes methodology to numeric prediction (i.e. regression) tasks, and compares it to linear regression, instance-based learning, and a method that produces “model trees” - decision trees with linear regression functions at the leaves. Although we exhibit an artificial dataset for which naïve Bayes is the method of choice, on real-world datasets it is almost uniformly worse than model trees. The comparison with linear regression depends on the error measure: for one measure naïve Bayes performs similarly, for another it is worse. Compared to instance-based learning, it performs similarly with respect to both measures. These results indicate that the simplistic statistical assumption that naïve Bayes makes is indeed more restrictive for regression than for classification.

text: Naive Bayes for regression Despite its simplicity, the naïve 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 naïve Bayes methodology to numeric prediction (i.e. regression) tasks, and compares it to linear regression, instance-based learning, and a method that produces “model trees” - decision trees with linear regression functions at the leaves. Although we exhibit an artificial dataset for which naïve Bayes is the method of choice, on real-world datasets it is almost uniformly worse than model trees. The comparison with linear regression depends on the error measure: for one measure naïve Bayes performs similarly, for another it is worse. Compared to instance-based learning, it performs similarly with respect to both measures. These results indicate that the simplistic statistical assumption that naïve Bayes makes is indeed more restrictive for regression than for classification.

year: 1998-10

title: A numerical classification of some Thermus isolates

authors: Hudson, J. Andrew

subjects: biology

abstract: A numerical classification was performed on a collection of 45 Thermus isolates recovered from New Zealand hot pools and on six type strains including T. aquaticus and "T. thermophilus". Unweighted average linking (UPGMA) and single linkage clustering methods were applied to similarity matrices derived from simple matching (SSM) and Jaccard similarity (SJ) coefficients. Differences were observed between phenograms derived from SSM and SJ coefficients, indicating that some of the clusters formed were derived from a significant component of negative matches. Test error was estimated at 2.9%. In the UPGMA/SSM phenogram, seven clusters were formed. A majority of the New Zealand isolates did not cluster with non-New Zealand isolates. Analysis of variance showed that there was a relationship between the composition of the clusters and the temperature and pH of the source of the isolate. Chi-squared testing showed that, within New Zealand, the geographical source of the isolate had no bearing on the clusters formed.

text: A numerical classification of some Thermus isolates A numerical classification was performed on a collection of 45 Thermus isolates recovered from New Zealand hot pools and on six type strains including T. aquaticus and "T. thermophilus". Unweighted average linking (UPGMA) and single linkage clustering methods were applied to similarity matrices derived from simple matching (SSM) and Jaccard similarity (SJ) coefficients. Differences were observed between phenograms derived from SSM and SJ coefficients, indicating that some of the clusters formed were derived from a significant component of negative matches. Test error was estimated at 2.9%. In the UPGMA/SSM phenogram, seven clusters were formed. A majority of the New Zealand isolates did not cluster with non-New Zealand isolates. Analysis of variance showed that there was a relationship between the composition of the clusters and the temperature and pH of the source of the isolate. Chi-squared testing showed that, within New Zealand, the geographical source of the isolate had no bearing on the clusters formed.

year: 1986

title: Taonga maioha: (Born this way) The in-depth stories of three Māori, Pasifika, and Indigenous Queens

authors: Tamuera Ariki, Paikea

abstract: Who else has the right to define these, Queens? 

This thesis provides a vital platform for the voices of three Māori, Pasifika, and Indigenous Queens, allowing them to share their transformation stories in their own words. Their experiences and feelings about in their words “being born into the wrong bodies” highlight the complex realities of identity and the struggle for acceptance by others. 

These Queens are unified in their desire for dignity and respect. Dignity and respect underscore a fundamental human right. They seek recognition in all areas of their lives, from healthcare to education, housing, employment, and beyond. By articulating their needs, they advocate for themselves and all marginalized individuals, seeking fairness and understanding within systems that often overlook them. 

Focusing on their lived experiences is crucial for fostering empathy and promoting systemic change, as it brings attention to the disparities they face and the importance of inclusivity in society. By sharing their truths, these Queens contribute to a broader dialogue about identity, respect, and the pursuit of equality for all. 

This thesis aims to illuminate the transformation stories or pūrākau of ten Māori and Pasifika Queens, to foster understanding of their unique experiences. The focus on those who identify as wahine despite being born biologically male allows the reader to highlight the rich diversity of gender identity within these cultures and groups. 

The Māori Ethics Committee granted ethics approval for the study, thereby paving the way for the Queens to tell their stories or pūrākau from their truths and perspectives. 

Taonga Maioha details the in-depth transformation stories of three Māori-Pasifika Queens aged 60 plus, supported by a contextual and cultural analysis of their generation. The voices and stories of the Queens are central to this thesis. These stories are thematically analyzed to unpack the cultural and generational influences impacting their identities. The core identity constructs of whakapapa, tinana, hinengaro, and wairua are used to frame and organize their transformation stories according to their instructions. Above all, this thesis is anchored in their rights to define themselves on the premise that they have the mana to define themselves irrespective of others who define them from the sidelines of their lives. Their definitions are theirs alone. The storytellers call themselves Queen, Wahine, or women. This is based on the assertion that it is their right to be called a Queen. In certain contexts, they may refer to themselves as takatāpui and transgender for the sake of acceptance by others. This thesis is written to enhance the mana of the storytellers. These are their stories and their truths. 

The late Dame Georgina Beyer, was one of the original participants in this thesis. Many interviews were conducted with her. Instructed by Georgina before her passing to publish elements of her story as part of this thesis. Unfortunately, the Ethics Committee at Waikato University did not accept this due to her passing. Hence, the reason ‘First Lady’ was introduced, where Liz Roberts gives exclusive details of her change, both legally and physically.

text: Taonga maioha: (Born this way) The in-depth stories of three Māori, Pasifika, and Indigenous Queens Who else has the right to define these, Queens? 

This thesis provides a vital platform for the voices of three Māori, Pasifika, and Indigenous Queens, allowing them to share their transformation stories in their own words. Their experiences and feelings about in their words “being born into the wrong bodies” highlight the complex realities of identity and the struggle for acceptance by others. 

These Queens are unified in their desire for dignity and respect. Dignity and respect underscore a fundamental human right. They seek recognition in all areas of their lives, from healthcare to education, housing, employment, and beyond. By articulating their needs, they advocate for themselves and all marginalized individuals, seeking fairness and understanding within systems that often overlook them. 

Focusing on their lived experiences is crucial for fostering empathy and promoting systemic change, as it brings attention to the disparities they face and the importance of inclusivity in society. By sharing their truths, these Queens contribute to a broader dialogue about identity, respect, and the pursuit of equality for all. 

This thesis aims to illuminate the transformation stories or pūrākau of ten Māori and Pasifika Queens, to foster understanding of their unique experiences. The focus on those who identify as wahine despite being born biologically male allows the reader to highlight the rich diversity of gender identity within these cultures and groups. 

The Māori Ethics Committee granted ethics approval for the study, thereby paving the way for the Queens to tell their stories or pūrākau from their truths and perspectives. 

Taonga Maioha details the in-depth transformation stories of three Māori-Pasifika Queens aged 60 plus, supported by a contextual and cultural analysis of their generation. The voices and stories of the Queens are central to this thesis. These stories are thematically analyzed to unpack the cultural and generational influences impacting their identities. The core identity constructs of whakapapa, tinana, hinengaro, and wairua are used to frame and organize their transformation stories according to their instructions. Above all, this thesis is anchored in their rights to define themselves on the premise that they have the mana to define themselves irrespective of others who define them from the sidelines of their lives. Their definitions are theirs alone. The storytellers call themselves Queen, Wahine, or women. This is based on the assertion that it is their right to be called a Queen. In certain contexts, they may refer to themselves as takatāpui and transgender for the sake of acceptance by others. This thesis is written to enhance the mana of the storytellers. These are their stories and their truths. 

The late Dame Georgina Beyer, was one of the original participants in this thesis. Many interviews were conducted with her. Instructed by Georgina before her passing to publish elements of her story as part of this thesis. Unfortunately, the Ethics Committee at Waikato University did not accept this due to her passing. Hence, the reason ‘First Lady’ was introduced, where Liz Roberts gives exclusive details of her change, both legally and physically.

year: 2024