问题如下：
After creating and analyzing the visualization, Steele is concerned that some tokens are likely to be noise features for ML model training; therefore, she wants to remove them.
To address her concern in her exploratory data analysis, Steele should focus on those tokens that have:
选项：
A.low chi-square statistics.
low mutual information (ML) values.
very low and very high term frequency (TF) values.
解释：
C is correct. Frequency measures can be used for vocabulary pruning to remove noise features by filtering the tokens with very high and low TF values across all the texts. Noise features are both the most frequent and most sparse (or rare) tokens in the dataset. On one end, noise features can be stop words that are typically present frequently in all the texts across the dataset. On the other end, noise features can be sparse terms that are present in only a few text files. Text classification involves dividing text documents into assigned classes. The frequent tokens strain the ML model to choose a decision boundary among the texts as the terms are present across all the texts (an example of underfitting). The rare tokens mislead the ML model into classifying texts containing the rare terms into a specific class (an example of overfitting). Thus, identifying and removing noise features are critical steps for text classification applications.
想问以下，噪声词的chi-square value和MI value有什么特点吗？ 或者说可以通过这两个值来判断是否是噪声词吗？谢谢
