Introduction
Chapter 1.Notes from research
Chapter 2.Courses
- 2.1.Python for Data Science and Machine Learning Bootcamp
  2.1.1.Virtual Environment
  2.1.2.Python crash course
  2.1.3.Python for Data Analysis - NumPy
  2.1.3.1.Numpy arrays
  2.1.3.2.Numpy Array Indexing
  2.1.3.3.Numpy Operations
  2.1.4.Python for Data Analysis - Pandas
  2.1.4.1.Introduction
  2.1.4.2.Series
  2.1.4.3.DataFrames
  2.1.4.4.Missing Data
  2.1.4.5.GroupBy
  2.1.4.6.Merging joining and Concatenating
  2.1.4.7.Data input and output
  2.1.5.Python for Data Visual Visualization - Pandas Built-in Data Visualization
  2.1.6.Python for Data Visualization - Matplotlib
  2.1.6.1.Introduction of Matplotlib
  2.1.6.2.Matplotlib
  2.1.7.Python for Data Visualization - Seaborn
  2.1.7.1.Introduction to Seaborn
  2.1.7.2.Distribution Plots
  2.1.7.3.Categorical Plots
  2.1.7.4.Matrix Plots
  2.1.7.5.Grids
  2.1.7.6.Regression Plots
  2.1.8. Python for Data Visualization - Plotly and Cufflinks
  2.1.8.1.Introduction to Plotly and Cufflinks
  2.1.8.2.Plotly and Cufflinks
  2.1.9. Python for Data Visualization - Geographical plotting
  2.1.9.1.Choropleth Maps - USA
  2.1.9.2.Choropleth Maps - World
  2.1.10.Combine data analysis and visualization to tackle real world data sets
  911 calls capstone project
  2.1.11.Linear regression
  2.1.11.1.Introduction to Scikit-learn
  2.1.11.2.Linear regression with Python
  2.1.12.Logistic regression
  2.1.12.1.Logistic regression Theory
  2.1.12.2.Logistic regression with Python
  2.1.13.K Nearest Neighbors
  2.1.13.1.KNN Theory
  2.1.13.2.KNN with Python
  2.1.14.Decision trees and random forests
  2.1.14.1.Introduction of tree methods
  2.1.14.2.Decision trees and Random Forests with Python
  2.1.15.Support Vector Machines
  2.1.16.K means clustering
  2.1.17.Principal Component Analysis
- 2.2. Machine Learning Crash Course Jam

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1. Introduction
2.Model evaluation

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2.1.12.1.Logistic regression Theory

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Last updated 4 years ago

1. Introduction

Logistic regression是一種用來做classification的方式
- Logistic regression讓我們可以解決預測離散類別 (discrete categories)的分類問題
- 傳統的二元分類問題有0或1兩個種類
我們不可以用Linear regression來做二元分類
- 擬合後會出現小於0或大於1的值
- Logistic function (Sigmoid function)的值域介於0跟1之間, 因此一般會使用Logistic regression

2.Model evaluation

在使用training data訓練完Logistic regression的模型後, 將會使用testing data來評估模型的performance
Confusion matrix
- 真正的精度 (accuracy)的算法：
  - (TP + TN)/total = 150/165 = 0.91
- Misclassification rate:
  - (FP + FN)/total = 15/165 = 0.09
- False Positive: Type 1 error
- False Negative: Type 2 error