Machine Learning: Random Forest with Python from Scratch - Importing Data, Helper Functions

The video tutorial covers importing a dataset using pandas, preparing it by converting it into a list, and shuffling it to ensure randomness. It then explains how to split the data into training and testing sets. The tutorial also introduces three helper functions: one to find unique values, another to count class occurrences, and a third to check if a value is numeric. These functions are essential for later stages of data analysis and machine learning tasks.

What library is used to import the dataset in the tutorial?

• NumPy

• Pandas

• Matplotlib

• SciPy

Why is it important to shuffle the dataset before processing?

• To increase the size of the dataset

• To ensure the data is in chronological order

• To prevent bias by randomizing the order of data

• To make the dataset easier to read

What method is used to shuffle the dataframe?

• sort

• groupby

• sample

• merge

How many records are used for training in the dataset?

• 800

• 891

• 1000

• 91

What is the purpose of splitting the data into training and testing sets?

• To reduce the size of the dataset

• To evaluate the model's performance

• To increase the dataset's complexity

• To simplify data visualization

What does the 'unique values' function return?

• A list of all values

• A set of unique values

• A dictionary of counts

• A sorted list of values

What data structure is used to store the count of classes?

• Set

• Dictionary

• Tuple

• List

What does the 'is NUM' function check for?

• If a value is a list

• If a value is a string

• If a value is numeric

• If a value is a boolean

Which of the following is considered a numeric type in the 'is NUM' function?

• String

• Boolean

• List

• Integer

What is the main purpose of the helper functions discussed?

• To assist in implementing a decision tree

• To export data to a file

• To visualize data

• To clean the dataset