title: The development of Holte's 1R Classifier

authors: Nevill-Manning, Craig G.

subjects: computer science

abstract: The 1R procedure for machine learning is a very simple one that proves surprisingly effective on the standard datasets commonly used for evaluation. This paper describes the method and discusses two areas that can be improved: the way that intervals are formed when discretizing continuously-valued attributes, and the way that missing values are treated. Then we show how the algorithm can be extended to avoid a problem endemic to most practical machine learning algorithms—their frequent dismissal of an attribute as irrelevant when in fact it is highly relevant when combined with other attributes.

text: The development of Holte's 1R Classifier The 1R procedure for machine learning is a very simple one that proves surprisingly effective on the standard datasets commonly used for evaluation. This paper describes the method and discusses two areas that can be improved: the way that intervals are formed when discretizing continuously-valued attributes, and the way that missing values are treated. Then we show how the algorithm can be extended to avoid a problem endemic to most practical machine learning algorithms—their frequent dismissal of an attribute as irrelevant when in fact it is highly relevant when combined with other attributes.

year: 1995-06

title: Using model trees for classification

authors: Frank, Eibe

subjects: model trees

abstract: Model trees, which are a type of decision tree with linear regression functions at the leaves, form the basis of a recent successful technique for predicting continuous numeric values. They can be applied to classification problems by employing a standard method of transforming a classification problem into a problem of function approximation. Surprisingly, using this simple transformation the model tree inducer M5’, based on Quinlan’s M5, generates more accurate classifiers than the state-of-the-art decision tree learner C5.0, particularly when most of the attributes are numeric.

text: Using model trees for classification Model trees, which are a type of decision tree with linear regression functions at the leaves, form the basis of a recent successful technique for predicting continuous numeric values. They can be applied to classification problems by employing a standard method of transforming a classification problem into a problem of function approximation. Surprisingly, using this simple transformation the model tree inducer M5’, based on Quinlan’s M5, generates more accurate classifiers than the state-of-the-art decision tree learner C5.0, particularly when most of the attributes are numeric.

year: 1997-04

title: Coaching for the game of life: Driving coaching with mana

authors: Were, Brandyn Māia

subjects: Kaupapa Māori

abstract: Coaching needs to account for culture. This indigenous autoethnography explores personal experience to show how I, a young Māori male coach in Aotearoa New Zealand, navigates the complex nature of coaching. By weaving together Kaupapa Māori, established coaching knowledge, and experiences from my playing and coaching journey to date, a Kaupapa Māori coaching framework emerges – Driving Coaching with Mana.

As I reflect on the teachings of my coach, Mr. Mel Young, combined with my cultural identity, I reconnect to critical moments in my life that have shaped the person I am today and the values I hold, elevating the centrality of culture through the practice of Kaupapa, Ako, Manaakitanga, Whanaungatanga, Wairuatanga, and self-reflection as Āta-kaiako (a reflective coach).

Driven to enhance the development of rangatahi (youth) through sport, and informed by literature and experience, Driving Coaching with Mana is personally and culturally grounded – a way of thinking about coaching differently and in a more holistic way. There is more to the people we coach than their lives as athletes. Coaching is more than skill acquisition.

text: Coaching for the game of life: Driving coaching with mana Coaching needs to account for culture. This indigenous autoethnography explores personal experience to show how I, a young Māori male coach in Aotearoa New Zealand, navigates the complex nature of coaching. By weaving together Kaupapa Māori, established coaching knowledge, and experiences from my playing and coaching journey to date, a Kaupapa Māori coaching framework emerges – Driving Coaching with Mana.

As I reflect on the teachings of my coach, Mr. Mel Young, combined with my cultural identity, I reconnect to critical moments in my life that have shaped the person I am today and the values I hold, elevating the centrality of culture through the practice of Kaupapa, Ako, Manaakitanga, Whanaungatanga, Wairuatanga, and self-reflection as Āta-kaiako (a reflective coach).

Driven to enhance the development of rangatahi (youth) through sport, and informed by literature and experience, Driving Coaching with Mana is personally and culturally grounded – a way of thinking about coaching differently and in a more holistic way. There is more to the people we coach than their lives as athletes. Coaching is more than skill acquisition.

year: 2023