Practical 9: Predictive Modelling Analysis and Classification Models
VerifiedAdded on 2021/05/27
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Practical Assignment
AI Summary
This practical assignment delves into the realm of predictive modelling, utilizing the Iris dataset as a case study. The assignment addresses key concepts such as feature selection, dataset splitting, and the importance of correlation analysis in identifying optimal features for classification tasks. It explores the performance of linear and kernel Support Vector Machines (SVMs), comparing their predictive accuracy and training speed. The analysis includes a discussion on the challenges of classifying the two moons dataset and the necessity of parameter selection, including the use of cross-validation, to enhance model performance. The assignment concludes with a comparison of different classification models and highlights the importance of parameter tuning for achieving optimal results in predictive modelling. The student's work provides valuable insights into the practical application of machine learning techniques and demonstrates an understanding of core concepts in data science.
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