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

The video tutorial explains how to build a decision tree by first identifying the best question to ask at each node using a function called 'best split'. It describes the role of leaf nodes, which contain predictions based on class counts. The tutorial covers the recursive process of tree construction, detailing how to handle true and false branches. It concludes with a discussion on using the constructed tree and hints at writing a custom fit method for model training, similar to a random forest classifier.

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 and gain for splitting

• To count the number of leaves in the tree

• To visualize the tree structure

What does the 'Leaf' class represent in the context of a decision tree?

• A node with no children

• The root node of the tree

• A node with maximum impurity

• A node that contains predictions

When does a node become a leaf node in the decision tree?

• When the number of nodes is even

• When the tree height is maximum

• When the gain is zero

• When the gain is maximum

What is the base case for recursion in the 'Buildtree' method?

• When the gain is zero

• When the root node is reached

• When the tree has more than 10 nodes

• When the tree is perfectly balanced

What is the role of the 'partition' method in tree construction?

• To merge different branches of the tree

• To split data into true and false rows

• To calculate the depth of the tree

• To determine the root node of the tree

What does the 'Buildtree' method ultimately return?

• The height of the tree

• The root node of the constructed tree

• The number of nodes in the tree

• The total gain of the tree

What is the next step after building the decision tree?

• Pruning the tree to reduce its size

• Calculating the accuracy of the tree

• Using the tree to train a model with a custom 'fit' method

• Visualizing the tree structure