You want to classify Twitter posts, or tweets, by sentiment. You have collected two months' worth of tweets which were labeled by volunteers as positive or negative. How would you split the data effectively into a training set and a validation set?Group of answer choicesYou should split at a single point in time; e.g., before or after a certain date.You should split by time of day; e.g., before or after noon every day.You should split by length of tweet; e.g., more or less than 40 characters.You should split uniformly at random.

You want to classify images of dogs from cats. You have collected 2,000 images of dogs and 2,000 images of cats. How would you split the data effectively into a training set and a validation set?Group of answer choicesYou should split by light or dark fur color.You should split by whether the image contains a cat or dog.You should split by high- or low-quality images.You should split uniformly at random.

Which of the following techniques should be used to determine whether the sentiment in a Facebook post is positive or negative? ClassificationRegression Traditional ProgrammingClustering

You want to predict if a hospital patient is in danger of cardiac arrest and needs to be moved to the ICU. You have collected data from multiple patients consisting of vital signs with labels of whether or not the patient experienced cardiac arrest. Some of the data was collected on the same patient who may have experienced multiple cardiac arrests at different times. How would you split the data effectively into a training set and a validation set?Group of answer choicesYou should split by age of patient.You should split uniformly at random by visit.You should split uniformly at random by patient.You should split by the presence of cardiac arrest in medical history.

Search for those tweets that contain either the word "COVID" or "pandemic". Save the output to a new dataframe called covid_tweets.Calculate the percentage (2 decimal places) of tweets that contain either the word "COVID" or "pandemic" and save this as "perc_covid". Use this value to create a sentence that says: "(perc_covid) % of tweets from Scott Morrison were about COVID or the pandemic". Save this sentence as a variable called answer.

Suppose you’re using an NLP system to learn how adult voters respond to a politician. What kind of operation would you have it perform? Question 4 options: Facial recognition Sentiment analysis Pattern matching Classification