# 1.3 机器学习基础（Basic Recipe for Machine Learning）

初始模型训练完成后，首先要知道算法的偏差高不高，如果偏差较高，试着评估训练集或训练数据的性能。如果偏差的确很高，甚至无法拟合训练集，要做的就是增加神经网络的隐藏层个数、神经元个数，训练时间延长，选择其它更复杂的NN模型等

如果网络足够大，通常可以很好的拟合训练集，一旦偏差降低到可以接受的数值，检查一下方差有没有问题，为了评估方差，要查看验证集性能，从一个性能理想的训练集推断出验证集的性能是否也理想，如果方差高，最好的解决办法就是增加训练样本数据，进行正则化Regularization，选择其他更复杂的NN模型

两点需要注意：

第一点，高偏差和高方差是两种不同的情况，通常用训练验证集来诊断算法是否存在偏差或方差问题，然后根据结果选择尝试部分方法。如果算法存在高偏差问题，准备更多训练数据没什么用处

第二点，在当前的深度学习和大数据时代，只要持续训练一个更大的网络，只要正则适度，通常构建一个更大的网络便可以在不影响方差的同时减少偏差，而采用更多数据通常可以在不过多影响偏差的同时减少方差。

这两步实际要做的工作是：使用更复杂的神经网络和海量的训练样本，一般能够同时有效减小Bias和Variance


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