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[New] Concise and Practical AI/ML
  • Pages
    • Preface
    • Artificial Intelligence
      • Concepts
      • High-level Intelligence
    • Maths for ML
      • Calculus
      • Algebra
    • Machine Learning
      • History of ML
      • ML Models
        • ML Model is Better
        • How a Model Learns
        • Boosted vs Combinatory
      • Neuralnet
        • Neuron
          • Types of Neurons
        • Layers
        • Neuralnet Alphabet
        • Heuristic Hyperparams
      • Feedforward
        • Input Separation
      • Backprop
        • Activation Functions
        • Loss Functions
        • Gradient Descent
        • Optimizers
      • Design Techniques
        • Normalization
        • Regularization
          • Drop-out Technique
        • Concatenation
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          Overfitting & Underfitting
        • Explosion & Vanishing
      • Engineering Techniques
    • Methods of ML
      • Supervised Learning
        • Regression
        • Classification
      • Reinforcement Learning
        • Concepts
        • Bellman Equation
        • Q-table
        • Q-network
        • Learning Tactics
          • Policy Network
      • Unsupervised Learning
        • Some Applications
      • Other Methods
    • Practical Cases
    • Ref & Glossary
[New] Concise and Practical AI/ML
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Overfitting & Underfitting
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Overfitting & Underfitting

What is Overfitting

Overfitting is when the neuralnet changes to adapt so much to a specific set of data.
Overfitting happens when
The number of training samples are too few
The number of neurons and layers are too much
Or both

Prevent Overfitting

Use Data Split

Split training data into training set and test set.

Use Regularisation

To limit weight range into a small range, the rest of the range for weight values should be for those training sample unknown yet.

Underfitting

Underfitting is the case that the model has not been trained enough. The model in such case doesn’t give good results for known data nor good results for unknown data.
⁠

What is Overfitting
Prevent Overfitting
Use Data Split
Use Regularisation
Underfitting
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