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 randomly shuffling it to ensure unbiased training and testing. It then demonstrates splitting the data into training and testing sets. The tutorial also includes writing helper functions to identify unique values, count class occurrences, and check for numeric values, which will be used in later stages of implementing a decision tree.

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

• SciPy

• Matplotlib

• Pandas

• NumPy

Why is it important to shuffle the dataset before splitting it?

• To make the dataset smaller

• To increase the size of the dataset

• To avoid bias by ensuring randomness

• To ensure the data is sorted

What is the purpose of converting the data frame into a list?

• To reduce data size

• To increase processing speed

• To perform operations more easily

• To save memory

How many records are used for training in the video?

• 891

• 100

• 800

• 91

What is the role of the 'unique values' function?

• To count the number of rows

• To find unique values in a column

• To split the dataset

• To sort the dataset

What data structure is used to store unique values in the 'unique values' function?

• List

• Dictionary

• Tuple

• Set

What does the 'class count' function return?

• The total number of rows

• The number of unique columns

• The count of each class label

• The average value of a column

Which built-in function is used to check if a value is numeric?

• isinstance

• len

• type

• str

What types of numbers does the 'is NUM' function consider?

• Both integers and floats

• Only complex numbers

• Only floats

• Only integers

What is the main purpose of the helper functions discussed in the video?

• To reduce dataset size

• To clean the dataset

• To assist in implementing a decision tree

• To visualize data