Inductive bias refers to assumptions made by learning algorithms to predict outputs for unseen data, guiding how models generalise from training examples.
What is Inductive Bias?
Without it, algorithms struggle to predict beyond training data, resulting in poor performance on new examples.
Importance of Inductive Bias
Occam's Razor, favouring simpler models Linearity assumptions in linear regression, where output relates linearly to input variables.
Examples of Inductive Bias
Relational (relationships between entities) Non-relational (techniques constraining algorithms)
Types of Inductive Bias
Finding the right balance is key. Too strong a bias can lead to overfitting, while too weak may cause underfitting, hindering effective learning.
Balancing Inductive Bias
A well-tuned inductive bias improves model accuracy and generalisation. Understanding these biases helps choose suitable algorithms for specific tasks.
Impact on Model Performance
Inductive bias is vital in Machine Learning. By managing these biases, practitioners can enhance model performance.
Conclusion