Machine Learning Random Forest with Python from Scratch - Accuracy and Error - Random Forest Step-by-Step

The video tutorial explains the implementation of an accuracy method to evaluate model performance. It covers the steps to calculate accuracy, analyze results, and introduces the concept of a random forest. The tutorial provides instructions on building a random forest by training multiple decision trees and highlights the benefits of using a random forest for improved accuracy through voting mechanisms.

What is the initial step in defining the accuracy method?

• Building a decision tree

• Printing the accuracy

• Calculating the total number of predictions

• Setting up a correct count variable

In the accuracy calculation process, what is compared to determine correctness?

• The predicted value and the actual value

• The actual value and the median value

• The predicted value and the maximum value

• The predicted value and the average value

What is the accuracy percentage achieved by the single decision tree?

• 90%

• 80%

• 60%

• 70%

What is the first step in creating a Random Forest?

• Combining predictions from multiple trees

• Shuffling the dataset and partitioning it

• Calculating the accuracy of a single tree

• Printing the final output

How does the Random Forest reduce the chance of error?

• By using a single tree with high accuracy

• By increasing the dataset size

• By using a complex algorithm for a single tree

• By averaging the predictions of multiple trees