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Reviewing the algorithms of popular classifiers in details and demonstrating the application with scikit-learn package with standardization and PCA to improve prediction performance.

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project-parkinsons-disease-classification

This notebook uses the dataset from UCI Machine Learning Repository and classification models to predict whether a patient has Parkinson's Disease, with a focus on modele improvement on Logistic Regression.

Overview

I. Data Preparation

  • Load and Oberserve Data
  • Data Preprocessing
  • Min-Max Normalization
  • Problem Definition

II. Modeling

Each modeling section consists of an brief intro, the algorithm, discussion on related topics, and application on the dataset.

  1. kNN
    • Non-parametric Models
    • Algorithm
  2. Naive Bayes
    • Bayes Classifier
    • Algorithm
    • Generative Model vs. Discriminative Model
  3. Logistic Regression
    • Sigmoid Function
    • Maximum Likelihood Estimation
    • Algorithm
    • (Also see Appendix A & B for related topics)
  4. Support Vector Machine
    • Convex Sets and Convex Hulls
    • Algorithm
    • Soft-Margin SVM
  5. Kernel SVM
    • Kernel
    • Mercer's theorem
    • RBF
    • Algorithm
  6. Decision Tree

III. Model Improvement

  • PCA
  • Pipeline

IV. Model Selection

  • ROC Curve
  • Change Threshold
  • Classification Report

V. Appendix

  • Appendix A: Concepts for Logistic Regression

    • A1. Binary Classification
    • A2. Log Odds
    • A3. Linear Discriminant Analysis
  • Appendix B: Linear Classifiers

    • B1. Definition
    • B2. Linear Separability
    • B3. Methods

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Reviewing the algorithms of popular classifiers in details and demonstrating the application with scikit-learn package with standardization and PCA to improve prediction performance.

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