# Lecture 6 Examples

📗 My handwriting is really bad, you should copy down your notes from the lecture videos instead of using these.
Lecture 6 Zoom Annotated (2021): Link
Lecture 6 Pre-recorded Annotated (from 2020, please use with caution): Link

# Q6 Quiz Instruction

📗 The quizzes must be completed during the lectures. Alternative ways to get the points are listed under "Grading Scheme" on the index page. No Canvas submissions are required. The grades will be updated at the end of the week on Canvas.

# Game Results

📗 Guess what percentage of the students did not start P1:

# Nearest Neighbors Demo

Data:

Column separator: , row separator: , label column index (first column is 0):
Comparison for binary left subtree: <= >= > < , use info-gain ratio? , default class:
Train
N (number of items): , M (number of features): , K (number of classes):
Initial info-gain table:
Number of nodes in the tree: , leaf nodes:

# Decision Tree Demo

📗 Training
Data:

Column separator: , row separator: , label column index (first column is 0):
Comparison for binary left subtree: <= >= > < , use info-gain ratio? , default class:
Train
N (number of items): , M (number of features): , K (number of classes):
Initial info-gain table:
Number of nodes in the tree: , leaf nodes:




📗 Validation
K Fold (-1 for Leave-One-Out):
Parameter name: , list of values:
Validate
Validation accuracy: , validation error:



📗 Testing
Test set:

Or use nested subsets to train: and the rest to test
Test
Predictions: , probabilities:
Test set accuracy: , error:
Confusion matrix:
Number of nodes:

# Entropy Demo

Start