Machine Learning: Random Forest with Python from Scratch - Accuracy and Error-2

The video tutorial explains how to implement an accuracy method to evaluate model performance. It details the steps to write an accuracy function, calculate accuracy, and interpret results. The tutorial then introduces the concept of a random forest, explaining how to create and train multiple decision trees to improve accuracy through voting. The benefits of using a random forest, such as error reduction, are discussed. The tutorial concludes with instructions for further implementation and offers help for combining trees into a random forest.

What is the initial step in defining the accuracy function?

• Setting the correct count to zero

• Calculating the total number of test data

• Printing the accuracy

• Converting keys to a list

What is the purpose of string interpolation in the accuracy function?

• To display the actual and predicted values

• To calculate the accuracy

• To convert keys to a list

• To iterate over test data

How is accuracy calculated in the function?

• By subtracting incorrect predictions from total values

• By adding correct count to total values

• By dividing correct count by total values and multiplying by 100

• By multiplying correct count by 100

What is the accuracy of the single decision tree mentioned in the video?

• 70%

• 80%

• 85%

• 75%

What is a key benefit of using a random forest over a single decision tree?

• It uses fewer computational resources

• It is easier to implement

• It requires less data

• It reduces the chance of error

What is the next step after creating a single decision tree?

• Creating another tree with different data

• Combining the tree with a support vector machine

• Testing the tree on the same data

• Implementing a neural network

How are different trees in a random forest trained?

• On randomly selected features

• On the same subset of data

• On different subsets of data

• On the entire dataset

What mechanism is used in a random forest to make a final prediction?

• Using the most complex tree

• Selecting the first prediction

• Majority voting

• Averaging predictions

What happens if one tree in a random forest makes a mistake?

• The tree is retrained

• The mistake is ignored

• Other trees can correct the mistake

• The entire forest fails

What is the suggested number of trees to train in a random forest?

• 3 trees

• 15 trees

• Only one tree

• As many as needed based on dataset size