Machine Learning Random Forest with Python from Scratch - How to Build a Tree

The video tutorial explains how to build a decision tree using a recursive approach. It covers the best split function to determine the optimal question for splitting data, the role of leaf nodes in making predictions, and the use of recursion to construct the tree. The tutorial also discusses the partition method for dividing data into true and false rows and concludes with the implementation of a custom fit method for training the model.

What is the purpose of the 'best split' function in building a decision tree?

• To calculate the total number of nodes in the tree

• To determine the best question to ask at each node

• To merge two trees into one

• To find the shortest path in the tree

When is a node considered a leaf node in the context of decision trees?

• When the node has no children

• When the node is at the root

• When the gain is zero

• When the gain is maximum

What does the partition method return when building a decision tree?

• The root node

• The total number of nodes

• The depth of the tree

• True and false rows

How does the recursive call in the build tree function differ for true and false branches?

• It uses different functions for each branch

• It passes all rows for both branches

• It passes only true rows for the true branch and false rows for the false branch

• It does not use recursion for false branches

What happens when the build tree function backtracks during recursion?

• It stops the recursion

• It executes the false branch code

• It returns to the root node

• It skips the false branch

What is the final output of the build tree function?

• A list of all nodes

• The maximum depth of the tree

• The root node of the decision tree

• The total number of leaves

What is the next step after building the decision tree, as mentioned in the video?

• Pruning the tree to reduce complexity

• Writing a custom fit method to train the model

• Calculating the accuracy of the tree

• Visualizing the tree structure