[Chew the book together] Vernacular interpretation of machine learning watermelon book——01 Introduction

Full book chapters

0101 Introduction to basic concepts

1.1 Introduction

What problem does machine learning solve?

• By means of calculation, use experience to improve the performance of the system itself
• With data
• Through some kind of learning algorithm
• Get the model
• Make predictions

1.2 Basic terminology

With data

• Data set , for example 100 watermelons
• Sample , for example 1 watermelon
• Feature vector The
  feature vector becomes the sample space.
  If the feature vector has color, size, and amplitude, then the dimension is three-dimensional, and the sample space is like a three-dimensional coordinate system.
• Attribute , one of the feature vectors, such as color

Through some kind of learning algorithm

• The process of learning a model from data is called learning or training . This process is completed by executing a learning algorithm

Get the model

Supervised learning	Unsupervised learning
The training data is marked with correct answers	The training data is unlabeled and there is no clear answer
Includes classification and regression	Include clustering

• Classification : the discrete value to be predicted, such as "good melon", "bad melon"

  • Two categories : only involves two categories-positive and negative, such as judging whether a melon is ripe or not, should it be picked?
  • Multi-category : The results involve multiple categories, for example, there are 3 types of watermelons: Black Beauty, Little Landmine, and Te Xiaofeng, which one to buy

• Regression : What I want to predict is a continuous value, such as watermelon sweetness 0.95, 0.37

  • You can also predict the price of watermelon in various time periods next year based on the price trend of watermelon in previous years
    

• Clustering : We don’t know how many categories to divide, the machine divides it by itself

  • Each group is called a " cluster " (cluster)

Make predictions

• Test: After learning the model, the process of using it to make predictions
• Test sample: the predicted sample
• Generalization ability: The ability of the learned model to apply to new samples. A model with strong generalization ability can be well applied to the real sample space

0102 Hypothetical space induction preference

1.3 Hypothetical space

Scientific reasoning

• Induction: special to general
• Deduction: general to special

1.4 Inductive Preference

The simpler the model, the more it reflects the essence,
just like a mathematical formula