Intro to ML

Review of Impurity

• Importance of Homework Submission

  • Homework submissions are crucial for preparation.

  • Some students achieved perfect scores (100%).

• Understanding Impurity

  • Impurity occurs when there are mixed classifications within a dataset.

  • Example: Evaluating qualification for a scholarship based on GPA and extracurricular participation, yielding classifications of 'Yes' (y) or 'No' (n).

• Gini Index (GI)

  • GI is used to measure the impurity of a dataset.

  • GI = 0 indicates pure classification (all y’s or all n's).

  • GI > 0 indicates impurity (mixed classifications).

Decision Trees

• Classification Process

  • Decision trees help classify data based on features.

  • Choose a feature to split the data (e.g., GPA vs. extracurriculars).

• Splitting Decisions

  • Two choices for split:

    • GPA greater than a threshold

    • Participation in extracurriculars

  • Aim to select the feature that results in the lowest impurity (Gini Index).

Calculation of Gini Index

• Example Analysis

  • For a given dataset containing pass/fail results:

    • Calculate proportions of class labels before splitting (will buy/will not buy).

    • Example: 4 of 8 will buy, 4 will not buy -> Gini = 0.5.

• Evaluation of Features

  • Each feature's impact on impurity measured by applying Gini calculation.

  • Example outputs for different classifications.

Machine Learning Overview

• Introduction to Machine Learning

  • Machine learning involves acquiring knowledge from data through experience and patterns.

  • Supervised learning involves known labels and inputs.

• Nonlinearity

  • Real-world problems often exhibit nonlinear relationships, complicating predictions.

  • Decision trees (simple, interpretable) versus more complex machine learning models (black boxes, less interpretable).

Features and Feature Vectors

• Definition of Features

  • Features are measurable properties input into the machine learning model to inform predictions.

• Label Importance

  • Labels are the intended outputs (e.g., passing an exam) that learning algorithms aim to predict.

• Representation of Features in Vectors

  • Features represented as vectors, including numerical columns and one-hot encoding for categorical variables (e.g., colors of fruit).

Classifying with Features

• Example Features

  • When predicting salary: relevant features might include education, job roles, etc.

  • Features must be identified clearly for successful predictions.

Homework Assignments

• Areas to Study

  • Review Gini indices and calculation processes.

  • Understand decision trees and how to determine which feature to split on.

  • Explore the fundamentals of machine learning and feature representation.

• Questions Encouraged

  • Students are encouraged to utilize Google Classroom to clarify doubts.