Inductive bias in machine learning refers to the set of assumptions that a learning algorithm uses to predict outputs given inputs it has not encountered. It is the bias that the model has towards certain outcomes based on the data it has been trained on and the algorithm's inherent design. Inductive bias helps the model generalize from the training data to unseen data.
For example, a decision tree algorithm has an inductive bias towards simple decision boundaries that are easy to explain and implement. This means that when faced with a complex problem, the decision tree may prefer to split the data in a way that results in a simpler tree structure, even if a more complex boundary would provide better accuracy.
In the context of cloud computing, services like Tencent Cloud offer machine learning platforms that allow developers to build and deploy models with various algorithms, taking into account their specific inductive biases to suit different types of problems and data.