Book Review - Hands on Machine Learning(C2-2)

All of materials(quotes, images, definitions) are from this book.
It’s all just for self-study.

Look at the big picture

Frame the Problem

• First question to ask : what exactly is the business objective
• How does the company expect to use and benfit from this model?

pipeline : a sequence of data processing components

• Second question to ask : what the current solution looks like (if any)
• will often give a reference performance, as well as insights on how to solve the problem

  • typical supervised learning task(with labeld data)
  • typical regression task(predict a value)
  • multiple regression problem : use multiple features to make a prediction
  • also a univariate regression problem : predict a single value for each district
  • plain batch learning

Select a Performance Measure

1. RMSE(Root Mean Square Error) : corresponds to the Euclidean norm
2. MAE(Mean Absolute Error) : corresponds to the Manhattan norm
   • The higher the norm index, the more it focuses on large values and neglects samll ones
     • This is why RMSE is more sensitive to outliers than the MAE
     • But when outliers are exponentially rare(like in a bell-shaped curve), the RMSE performs very well and is generally preferred

Check the Assumptions

• We need actual values of each district for our prediction(not category e.g., “cheap”,”medium”,”expensive)

Get the Data & Create the Workspace

• I’m gonna use google colab instead using jupyter notebook

Download the Data

• From this part, I’ll upload this notebook on my github

This is my github repository