Common statistical concepts in the supervised Machine Learning arena.

Area of Science:

• Machine Learning
• Statistics
• Data Science

Background:

• Statistics form the bedrock of Machine Learning (ML), underpinning its core principles and functionalities.
• Objective assessment of ML model performance relies heavily on appropriate statistical measurements.
• The broad scope of statistics in ML necessitates focused reviews on specific areas.

Purpose of the Study:

• To elucidate the critical role of statistical concepts in Machine Learning.
• To concentrate on common statistical principles relevant to supervised ML tasks.
• To examine the interdependencies and limitations of these statistical methods.

Main Methods:

• Review of foundational statistical concepts applicable to supervised ML.
• Analysis of statistical measurements for evaluating ML model performance.
• Exploration of interdependencies and limitations within supervised ML statistics.

Main Results:

• Identified statistics as an indispensable component of ML development and evaluation.
• Highlighted the importance of statistical rules for supervised learning models like classification and regression.
• Discussed the practical implications and constraints of applying statistical methods in ML.

Conclusions:

• Emphasizes the inseparable relationship between statistics and Machine Learning.
• Underscores the necessity of statistical understanding for effective supervised ML.
• Suggests further research into the nuanced application of statistics in ML.