Predictive Analytics with TensorFlow 6.3: Using BOW for Predictive Analytics

The video tutorial covers predictive analytics, focusing on the Bag of Words model for text representation. It explains building a spam classifier using logistic regression, detailing data preprocessing, model training, and evaluation. The tutorial highlights the importance of handling skewed datasets and optimizing model accuracy.

What is the primary purpose of the bag of words model in text analytics?

• To enhance the grammatical structure of text

• To translate text into multiple languages

• To simplify text representation by ignoring grammar

• To maintain the order of words in a document

In the context of spam classification, what is the main goal of using a logistic regression model?

• To classify messages as spam or not spam

• To count the number of words in a message

• To determine the language of a message

• To predict the length of a message

Why is it important to normalize text data before training a model?

• To convert text into numerical data

• To add more punctuation to the text

• To increase the number of unique words

• To reduce vocabulary size and handle case sensitivity

What is the role of one-hot encoding in the preprocessing of text data?

• To convert text into a binary format

• To translate text into another language

• To create a unique index for each word

• To remove special characters from text

Which TensorFlow function is used to map word indices to one-hot encoded vectors?

• tf.concat

• tf.one_hot

• tf.reduce_sum

• tf.embedding_lookup

What is the purpose of using a gradient descent optimizer in logistic regression?

• To convert categorical data into numerical data

• To minimize the loss function and improve accuracy

• To add more features to the model

• To increase the complexity of the model

How is the accuracy of the logistic regression model evaluated?

• By checking the number of features used in the model

• By counting the number of words in each message

• By comparing predicted and actual labels on the test set

• By measuring the time taken to train the model

What is a potential issue with the vocabulary created from the dataset?

• It may be too small to be useful

• It might be skewed towards non-spam words

• It could contain too many special characters

• It may not include enough unique words

What is a possible solution to address the skewness in the vocabulary?

• Increase the number of spam messages

• Add more punctuation to the text

• Allow unlimited text length

• Use a different encoding method

What will be discussed in the next video following this tutorial?

• Advanced neural networks

• TF-IDF model for predictive analytics

• Decision trees

• Support vector machines