Which statements about Naive Bayes are true? (Select all that apply.)  多项选择题

A

NB assumes feature independence

B

NB sums TF-IDF scores and picks highest

C

“48” indicates bullish predicted bearish

D

TF-IDF is required because NB cannot use raw counts

E

NB predicts using posterior probability calculations

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What will be the prediction of Naïve Bayes for the new item?  

You are asked to classify an email containing “Gentle Customer” as Ham or Spam using a Naive Bayes spam filter. You are given a corpus of 24 emails, 16 of which are Ham and 8 are Spam The frequencies of words are the following: - Ham: Gentle: 16 Customer: 10 Actual: 6 Cash: 2 - Spam: Gentle: 4 Customer: 2 Actual: 0  Cash: 8 Estimate prior, conditional, and posterior probabilities. What is the probability that the email containing "Gentle Customer" is classified as Ham without performing any post-processing steps like normalization? Note: the probability that the email containing "Gentle Customer" is classified as Ham should be obtained as P(“Gentle Customer” | Ham) x P(“Gentle Customer”) as seen in the examples in class, without dividing the result by the class representation of Ham.

给定下表中的训练数据集，我们要训练一个二进制分类器。表中，最后一列是二进制类标签，前四列都是二进制特征，每一行是训练示例。 假设使用 MLE 估算朴素贝叶斯分类器的参数。现在给定一个测试示例   𝑋 = ( 1 , 1 , 1 , 0 )  ，你的分类器将会预测的类标签是什么？   Given the training data set in the following table, we want to train a binary classifier. In the table, the last column is the binary class label, each of the first four columns is a binary feature, and each row is a training example. Suppose we use MLE to estimate parameters for a Naïve Bayes Classifier. Now given a test example   𝑋 = ( 1 , 1 , 1 , 0 )   , what is the class label your classifier will predict?

给定下表中的训练数据集，我们要训练一个二进制分类器。表中，最后一列是二进制类标签，前四列都是二进制特征，每一行是训练示例。 使用 MLE 估算朴素贝叶斯分类器的参数，你对  𝑃 ( 𝑋 2 = 1 | 𝑌 = 0 )   的估计值是多少？   Given the training data set in the following table, we want to train a binary classifier. In the table, the last column is the binary class label, each of the first four columns is a binary feature, and each row is a training example.  Using MLE to estimate parameters for a Naïve Bayes Classifier, what is your estimation for 𝑃 ( 𝑋 2 = 1 | 𝑌 = 0 ) ?

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