Successfully Evaluating Predictive Modelling
Successfully Evaluating Predictive Modelling
From the University of Edinburgh with Dr Xuefei Lu, Dr Johannes De Smedt and Dr Zexun Chen.
These videos are free, open resources unless otherwise stated, originally created for our 'Successfully Evaluating Predictive Modelling' short online course. The course is no longer running but you can still work through the resources in your own time using the videos in this channel.
These videos will give you an in-depth understanding of evaluation and sampling approaches for effective predictive modelling using Python. You will learn how to analyse the accuracy and quality of a predictive model, implement effective measures and strategies to measure models, evaluate datasets to determine appropriateness and strength of techniques and understand the techniques used in recommended systems. A background in mathematics and statistics is recommended and previous experience with a procedural programming language is beneficial (e.g. Python, C, Java, Visual Basic).
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User guide for coding platform Vocareum
How to use Vocareum
From Lizzy Garner-Foy
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Case Study Conclusion - Week 6
Case Study Conclusion
From Lizzy Garner-Foy
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Case Study Development - Week 6
Case Study Development
From Stuart Brett
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Under- and Oversampling - Week 4
Under- and Oversampling
From Calum Macphail
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Training, Validation and Test Sets - Week 4
Training, Validation and Test Sets
From Calum Macphail
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Association Metrics - Week 2
Association Metrics
From Calum Macphail
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Case Study Introduction - Week 6
Case Study Introduction
From Calum Macphail
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Singular Value Decomposition - Week 3
Singular Value Decomposition
From Lizzy Garner-Foy
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Confusion Matrix Metrics - Week 1
Confusion Matrix Metrics
From Calum Macphail
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