Machine Learning 에서 사용하는 기초 수학

ML: 데이터와 통계로 컴퓨터를 학습시킴. (데이터를 분석해서 결과를 예측함) Python Machine Learning (w3schools.com)

Python Machine Learning

데이터의 종류:

• Numerical
• Categorical
• Ordinal

Numerical data :숫자 

• Discrete Data
  - numbers that are limited to integers. Example: The number of cars passing by.
• Continuous Data
  - numbers that are of infinite value. Example: The price of an item, or the size of an item

Categorical data : 비교 불가 데이터. values that cannot be measured up against each other. Example: a color value, or any yes/no values.

 

Ordinal data : 상대적으로 비교 가능한 데이터. like categorical data, but can be measured up against each other. Example: school grades where A is better than B and so on.

 

데이터 타입을 알아야 어떻게 분석할지 알 수 있다. 

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• Mean - The average value
• Median - The mid point value
• Mode - The most common value

예제:

import numpy
from scipy import stats

speed = [99,86,87,88,111,86,103,87,94,78,77,85,86]


mean = numpy.mean(speed)
median = numpy.median(speed)
mode = stats.mode(speed)
standard_deviation = numpy.std(speed)

print(mean)
print()
print(median)
print()
print(mode)
print()
print(standard_deviation)

#The mode() method returns a ModeResult object that contains the mode number (86), and count (how many times the mode number appeared (3)).

결과:

89.76923076923077

 

87.0

 

ModeResult(mode=array([86]), count=array([3])) 

 

9.258292301032677

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Standard Deviation

정의: 각 값이 서로 얼마나 퍼져있는지 확인. (mean value를 기준으로)

예제는 상단에 있음