Successfully Evaluating Predictive Modelling
Successfully Evaluating Predictive Modelling
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).
From the University of Edinburgh with Dr Xuefei Lu, Dr Johannes De Smedt and Dr Zexun Chen.
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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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