1.With small training dataset, it’s easier to find a hypothesis to fit the training data exactly i.e. overfitting.
2. We can see this from the bias-variance trade-off. When hypothesis space is small, it has higher bias and lower variance. So with a small hypothesis space, it’s less likely to find a hypothesis to fit the data exactly i.e. underfitting.