[PYTHON] 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

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