Supervised learning (classification)

Classification

I write a memo about the classification of supervised learning, which I mainly studied using Aidemy.

Method

▼ Logistic regression

■ Overview

--Find and classify the boundaries of linearly separable data by learning

■ Features

--The border becomes a straight line --Used for data with few classes such as binary classification --Computable probability of data being classified into classes (precipitation probability, etc.)

■ Disadvantages

--Cannot be classified unless it is linearly separable --Not suitable for high-dimensional sparse data --Borderline is not versatile (pulled by training data)

■ Import method

from sklearn.linear_model import LogisticRegression --scikit-learn library --linear_module submodule --LogisticRegression () function

▼ Linear SVM

■ Overview

--Classified by data boundaries --SVM that draws a straight line to classify

■ Features

--Support vector (data group close to other classes) --Draw a border at the position where the distance from the support vector is the largest --Easy to generalize and improve data classification prediction --Easy to make a line

■ Disadvantages

――Forecast tends to be slow as the amount of data increases --Cannot be classified unless it is linearly separable

■ Import method

from sklearn.svm import LinearSVC

Recommended Posts

Supervised learning (classification)
Python: Supervised Learning (Classification)
Supervised learning 1 Basics of supervised learning (classification)
Supervised machine learning (classification / regression)
Machine learning classification
Machine Learning: Supervised --AdaBoost
Supervised learning (regression) 1 Basics
Python: Supervised Learning (Regression)
Machine learning / classification related techniques
Machine Learning: Supervised --Linear Regression
Python: Supervised Learning: Hyperparameters Part 1
Supervised Learning 3 Hyperparameters and Tuning (2)
Supervised learning (regression) 2 Advanced edition
Machine Learning: Supervised --Support Vector Machine
Supervised learning ~ Beginner's memo ~ (scikit-learn)
Supervised learning 2 Hyperparameters and tuning (1)
Machine Learning: Supervised --Decision Tree
Supervised word-to-word relationship classification using Word2Vec
Machine learning with python (1) Overall classification
Machine Learning: Supervised --Linear Discriminant Analysis
real-time-Personal-estimation (learning)
Deep learning learned by implementation 2 (image classification)
Learning record
Learning record # 3
Learning record # 1
Machine learning
Machine learning algorithm (implementation of multi-class classification)
[Machine learning] Supervised learning using kernel density estimation
Learning record # 2
6/10 Learning content
Deep Learning
numpy-sigmoid learning
EV3 x Pyrhon Machine Learning Part 3 Classification
Chapter 6 Supervised Learning: Classification pg212 ~ [Learn by moving with Python! New machine learning textbook]
Classification of guitar images by machine learning Part 1
Basics of Supervised Learning Part 1-Simple Regression- (Note)
Python & Machine Learning Study Memo ⑤: Classification of irises
[Machine learning] Supervised learning using kernel density estimation Part 2
Machine learning algorithms (from two-class classification to multi-class classification)
[Machine learning] Supervised learning using kernel density estimation Part 3
Classification of guitar images by machine learning Part 2
Basics of Supervised Learning Part 3-Multiple Regression (Implementation)-(Notes)-