Prediction

When we are interested mainly in the predicted variable as a result of the inputs, but not on the each way of the inputs affect the prediction. In a real estate example, Prediction would answer the question of: Is my house over or under valued? Non-linear models are very good at these sort of predictions, but not great for inference because the models are much less interpretable.

当我们感兴趣的是未知输入所对应的预测值，而不是输入输入是如何影响预测值的时候，这就是一个预测问题。比如在一个房地产的例子中，预测问题将回答这样的问题：我的房产是否高估或低估了价值？非线性模型在这种问题上往往表现得很好，但这种模型因为解释性很差，所以并不适合推断问题。

Inference

When we are interested in the way each one of the inputs affect the prediction. In a real estate example, Inference would answer the question of: How much would my house cost if it had a view of the sea? Linear models are suited for inference because the models themsleves are easier to understand than their non-linear counterparts.

当我们关心的是每一个输入如何影响预测值时，这就是推断问题。在真实的房地产例子中，推断问题将回答这样的问题：如果我的房子是海景房，将能卖多少钱？线性模型非常适合这类推断问题，因为这些模型本身很简单，相对于非线性模型来说也更好理解。