INTRODUCTION

In Machine Learning, building a model is not just about data. You also need to control how the model learns. This is done using hyperparameters. Hyperparameters are important settings that help improve model performance and accuracy.

  1. WHAT IS MODEL HYPERPARAMETER

1 Hyperparameter is a value set before training the model
2 It controls how the model learns from data
3 It is not learned by the model, it is decided by the user

Simple Example
1 Learning rate
2 Number of iterations
3 Depth of model

  1. HYPERPARAMETER VS PARAMETER
NoFeatureParameterHyperparameter
1DefinitionLearned by modelSet before training
2ControlAutomaticManual
3ExampleWeights, biasLearning rate, epochs
4RoleHelps predictionControls learning process
  1. TYPES OF HYPERPARAMETERS

A Model Hyperparameters
1 Control model structure
2 Example → number of layers

B Training Hyperparameters
1 Control learning process
2 Example → learning rate, batch size

  1. COMMON HYPERPARAMETERS

1 Learning Rate
A Controls how fast model learns

2 Epochs
A Number of times model sees data

3 Batch Size
A Number of samples per training step

4 Number of Layers
A Used in neural networks

5 Regularization
A Prevents overfitting

  1. HOW HYPERPARAMETERS WORK

1 Set hyperparameters before training
2 Model starts learning
3 Adjust values if performance is poor
4 Repeat until best result achieved

  1. SIMPLE WORKING EXAMPLE

1 Learning rate too high → model unstable
2 Learning rate too low → slow learning
3 Balanced value → best performance

  1. HYPERPARAMETER TUNING

1 Process of finding best values
2 Improves accuracy
3 Methods

A Manual tuning
B Grid search
C Random search

  1. ADVANTAGES

1 Improves model performance
2 Controls training process
3 Reduces errors

  1. DISADVANTAGES

1 Time-consuming
2 Requires experimentation
3 Needs experience

CONCLUSION

Hyperparameters are essential for building effective Machine Learning models. They control how the model learns and directly affect performance. By understanding and tuning hyperparameters properly, you can create accurate and efficient AI systems.