判断题 MSE（Mean Squared Error）是均方误差，又称均方差，是n个数据预测结果误差平方的均值，是线性回归模型最常用的损失函数。

判断题 损失函数（Loss Function）是指用数学的方法衡量假设函数预测结果与真实值之间的误差。机器学习算法的任务就是要使这个损失函数的差距越来越小，达到越来越准确的目标。

判断题 假设函数（Hypothesis Function）是指用数学的方法描述自变量（Xi）和因变量（Yi）之间的关系，它们之间可以是一个线性函数或非线性函数。

单项选择题 Which following one is false？（）

判断题 Is the following statement true or false? "The evaluation function is a function used to estimate the value or goodness of a position in minimax and related algorithms, and typically desigened to prioritize spped over accuracy."

单项选择题 "The error rate is usually higher on the testing set than on the training set." Is it true？（）

单项选择题 Which of these is a reasonable definition of machine learning？（）

单项选择题 Suppose you are working on stock market prediction. You would like to predict whether or not a certain company will win a patent infringement lawsuit (by training on data of companies that had to defend against similar lawsuits). Would you treat this as a classification or a regression problem？（）

单项选择题 An algorithm capable of categorizing any future pie into one of the two classes (positive and negative) is（）