Building a Decision tree

Q&A
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#1

Screen Link:

What I expected to happen:
In step 4, it was determined that Marital Status is the best column to split on.

What actually happened:
When running the ID3 algorithm, it kept splitting on Age.

#2

Hello @eykei, welcome to the community!

Please share your code so people can help you.

#3

Hi,
My code is basically the same as the solution. The question is more about the concept.

def print_with_depth(string, depth):
    # Add space before a string
    prefix = "    " * depth
    # Print a string, and indent it appropriately
    print("{0}{1}".format(prefix, string))
    
    
def print_node(tree, depth):
    # Check for the presence of "label" in the tree
    if "label" in tree:
        # If found, then this is a leaf, so print it and return
        print_with_depth("Leaf: Label {0}".format(tree["label"]), depth)
        # This is critical -- without it, you'll get infinite recursion
        return
    # Print information about what the node is splitting on
    print_with_depth("{0} > {1}".format(tree["column"], tree["median"]), depth)
    
    # Create a list of tree branches
    branches = [tree["left"], tree["right"]]
    
    for branch in branches:
        depth += 1
        print_node(branch, depth)
    # Insert code here to recursively call print_node on each branch
    # Don't forget to increment depth when you pass it in

print_node(tree, 0)
#4

In step 4, we run the find_best_column() function on the entire income dataframe.

In step 7, we run the id3() function on the smaller, pretend dataframe, called data that was created in step 5.