CFA一级数量题目分享Text analytics,ML.Learning Module 11 Introduction to Big Data Techniques

第一题：

Text analytics is appropriate for application to:

A large, structured datasets

B public but not private information

C identifying possible short-term indicators of coming trends

解析：

C is correct. Through the text analytics application of NLP, models using NLP analysis might incorporate non-traditional information to evaluate what people are saying—through their preferences, opinions, likes, or dislikes— in an attempt to identify trends and short-term indicators—for example, about a company, a stock, or an economic event—to forecast coming trends that may affect investment performance in the future.

C是正确的。通过NLP的文本分析应用，模型使用NLP分析非传统信息来评估人们说什么，比如通过分析他们的偏好、意见、喜欢或不喜欢，尝试识别趋势和短期指标，比如关于一个公司，一只股票，或经济事件预测未来趋势，这些趋势可能会影响投资表现。

第二题：

Which of the following statements is true in the use of ML:

A some techniques are termed “black box” due to data biases

B human judgment is not needed because algorithms continuously learn from  data

C training data can be learned too precisely, resulting in inaccurate predictions  when used with different datasets

解析：

C is correct. Overfitting occurs when the ML model learns the input and target dataset too precisely. In this case, the model has been“overtrained”on the data and is treating noise in the data as true parameters. An ML model that has been overfitted is not able to accurately predict outcomes using a different dataset and might be too complex.

C是正确的。当机器学习模型对输入和目标数据集的学习过于精准时，就会发生过拟合。在这种情况下，模型对数据进行了“过度训练”，并将数据中的噪声作为真实参数来处理。一个被过度拟合的模型会给出错误的结果。机器学习在理解底层数据和选择适当的数据分析技术时仍然需要人类的判断。由于它们没有明确编程，ML技术可能看起来是不透明的或“黑盒”方法，它们得到的结果可能不能完全理解或解释。