NO.PZ2022120201000001
问题如下：
a. What are the main differences between machine learning and more conventional econometric techniques?
b. For what kinds of problems would machine learning likely be more suitable than conventional econometric modeling?
解释：
a. Under conventional econometric approaches, the researcher selects a particular model or hypothesis and tests whether it is consistent with available data.There is an emphasis on establishing causality. Under machine-learning approaches, the emphasis is on letting the data decide the features to include in the model, with very few assumptions or theory. Establishing causality is less important. Instead, the focus is on the model’s prediction or classification accuracy.
b. Machine-learning techniques have advantages when applied to problems where there is little theory regarding the nature of a relationship or which features are relevant. It is used when the number of data points and the number of features are large. Machine learning might also be preferable when the relationships between features (and targets) are nonlinear.
老师您好，这里的causality，因果关系，是不是就是input和output之间的一个模型？比如Y=a+bX或者Y=aX^2类似这种的？
如果我理解的对的话，那么机器学习就是自己去寻找input和output之间的模型？而传统分析就是先把模型假设出来，再通过回归找到系数？
