相关考题
- 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（）
