Once you have used your training data, you will move on to testing/evaluating your model using the test dataset you prepared earlier. This is where we find out how well our model works against data that it has not previously seen. If our model fails here, we return to go, do not collect $200, and refine our process!
- Title Page
- Copyright and Credits
- Packt Upsell
- Contributors
- Preface
- Machine Learning Basics
- Introduction to machine learning
- Data mining
- Artificial Intelligence
- Bio-AI
- Deep learning
- Probability and statistics
- Approaching your machine learning project
- Iris dataset
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Build, buy, or open source
- Additional reading
- Summary
- References
- ReflectInsight – Real-Time Monitoring
- Bayes Intuition – Solving the Hit and Run Mystery and Performing Data Analysis
- Risk versus Reward – Reinforcement Learning
- Fuzzy Logic – Navigating the Obstacle Course
- Color Blending – Self-Organizing Maps and Elastic Neural Networks
- Facial and Motion Detection – Imaging Filters
- Encyclopedias and Neurons – Traveling Salesman Problem
- Should I Take the Job – Decision Trees in Action
- Deep Belief – Deep Networks and Dreaming
- Microbenchmarking and Activation Functions
- Intuitive Deep Learning in C# .NET
- Quantum Computing – The Future
Model evaluation
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