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 classification. It explains how to preprocess data, train a logistic regression model using TensorFlow, and evaluate its performance in classifying SMS messages as spam or ham. The tutorial highlights the importance of data normalization and the challenges of skewed datasets. It concludes with a brief introduction to using TF-IDF for further predictive analytics.

What is the primary purpose of the Bag of Words model in text analysis?

• To translate text into different languages

• To maintain the order of words in a text

• To simplify text by representing it as a set of unique words

• To analyze the grammatical structure of sentences

In the context of spam classification, what is the main goal of the model?

• To count the number of words in a message

• To classify messages as spam or not spam

• To determine the length of a message

• To predict the sender of a message

What is a key characteristic of the SMS dataset used for spam classification?

• It contains only non-spam messages

• It contains only spam messages

• It is skewed with a small percentage of spam messages

• It is evenly balanced between spam and non-spam messages

Why is text normalization important in data preprocessing for spam prediction?

• To increase the number of unique words

• To convert text into uppercase

• To reduce the potential vocabulary size

• To add more punctuation to the text

What is the purpose of using one-hot encoding in the preprocessing step?

• To remove special characters from text

• To translate text into multiple languages

• To increase the size of the dataset

• To convert text into numerical indices

In setting up the logistic regression model, why is the input placeholder of type integer?

• To perform arithmetic operations

• To handle text data directly

• To look up row indices in the identity matrix

• To store floating-point numbers

What function is used as the prediction operation in the logistic regression model?

• Tanh function

• Sigmoid function

• Softmax function

• ReLU function

What was the observed accuracy of the model after 75 iterations?

• 85%

• 90%

• 88%

• 97%

What is a potential issue with allowing unlimited text length in the model?

• It may increase the model's accuracy

• It may lead to overfitting

• Spammers might exploit it to create long texts

• It may reduce the vocabulary size

What is a suggested improvement for handling skewed vocabulary in the dataset?

• Add more spam messages to the dataset

• Allow unlimited text length

• Use frequency of word occurrences for embedding

• Increase the batch size