Notes on Python Machine Learning - Chapter 01

This is the notes of the first chapter of the book Python Machine Learning.
It introduces three types of machine learning tasks and the common pre-steps of these tasks.

1. Three Types of ML

type	feature	task
Supervised Learning	Labeled data Direct feedback Predict outcome/future	Classification Regression
Unsupervised Learning	No labels No Feedback Find hidden data structure	Clustering Dimensionality Reduction
Reinforcement Learning	Decision process Reward System Learn series of actions	Interactive problems

2. Pre-Steps of ML

2.1 Process Data

• Extract meaningful features
• Scale features value range
• Dimensionality reduction

2.2 Split SubSets

• Train
  • The training set is used to train the model
  • 70-80%
• Validation
  • tune the model’s hyperparameters and to make decisions about the model’s structure
  • 10-15%
• Test
  • provide an unbiased evaluation of the final model
  • 10-15%

2.3 Choose Performance Metrics

• Accuracy
  • $\frac{TP + TN}{TP + TN + FP + FN}$
  • the proportion of predictions that your model got right, among all predictions it made
  • accuracy can be misleading if your classes are imbalanced among different classes
• Precission
  • $\frac{TP}{TP + FP}$
  • the proportion of true positive predictions among all positive predictions
  • tend to be higher if the model tends to make less true positive predictions
  • it says nothing about the items it failed to label as positive
  • suitable if you want to avoid false positives(identifying spam emails)
• Recall
  • $\frac{TP}{TP + FN}$
  • the proportion of true positive predictions among all actual positives
  • tend to be higher if the model tends to make more true positive predictions
  • it says nothing about the items it falsely labeled as positive
  • suitable if you want to avoid false negatives(identifying disease)
• F1 Score
  • $2\frac{Precision * Recall}{Precision + Recall}$
  • balance precision and recall