Open noramcgregor opened 2 years ago
Metadata: Objectives
para 1, suggested rewrite: "Predicting a numerical value is known as Regression. Francis Galton coined the term Regression in the 19th century to describe a biological phenomenon: over generations, the heights of descendants of tall ancestors tend to fall towards a normal average. This phenomenon is also known as regression toward the mean (Regression Analysis)."
para 3, sentence 1: remove comma after "like"
para 3, sentence 2: add comma after "month"
para 3, sentence 4, suggested rewrite: "There are two categories (Yes or No), so this is a binary classification task." (clarity)
Metadata: Questions
Objectives
(throughout para 5, replace "tweet" with "social media post" to futureproof?)
para 5, bullet point 1, suggested rewrite: "We want to predict online shop visitors based on the number of social media marketing posts about the shop sent out in a month." (add full stop, update "tweets")
para 5, bullet point 1.1, sentence 2, suggested rewrite: "The first parameter is the expected number of visitors when there are no social media posts made." (add "number of" before "visitors", update "tweets")
para 5, bullet point 1.1, sentence 3, suggested rewrite: "The second is the slope: how much the number of visitors increases (or decreases) with each social media post." (replace hyphen with colon, correct "how much visitors", update "tweet")
para 5, bullet point 1.2, sentence 1 suggested rewrite: "The conceptual model is a linear model. With a few past examples of visitor numbers and social media posts, we can use the least squares algorithm to find the parameters which best predict the effect of increased posts" (break into two sentences, replace "tweets" with "posts")
para 5, bullet point 1.2, sentence 2, suggested rewrite: "The trained model consists of the two parameters and a formula using them to convert social media posts into visitors" (update "tweets")
para 5, bullet point 1.3, sentence 2: change "tweets" to "social media posts"
para 5, bullet point 1.3, sentence 3: add "meaning" after comma
para 5, bullet point 1.3, sentence 5: change "tweets" to "social media posts", add comma after "training data"
para 5, bullet point 1.4: add comma after "available"
para 6, sentence 2: change comma to semicolon (alternatively, change the full stop at the end of sentence 1 to a colon)
para 6, sentence 3: add comma before "but"
para 6, sentence 6: remove comma after "used", add comma after "tender)"
para 7: (bullet point 1 should be 2!)
para 7, bullet point 1: add full stop to end of sentence.
para 7, bullet point 1.1, sentence 4: add comma after "descent"
para 7, bullet point 1.1, sentence 5, suggested rewrite: "The algorithm finds the parameter values that best map input to output, with "best" meaning the ones that make the fewest mistakes." (clarity, correct "which" to "that", "best" in quotation marks)
para 7, bullet point 1.2, sentence 2: remove comma after "data"
Would you want to put abbreviations in capitals? Then change ml into ML like here https://carpentries-incubator.github.io/machine-learning-librarians-archivists/06-applying-machine-learning/index.html#use-cases-for-machine-learning-in-your-organization.
Intro: