sklearn
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SkLearn.Pipeline 에 대해 알아보자머신러닝(MACHINE LEARNING)/코드 리뷰(Code_Review) 2021. 4. 24. 01:11
파이프 라인 이란. The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a ‘__’, as in the example below. A step’s estimator may be replaced entirely by setting the parameter with its name to another estimator, or a..
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간단한 LinearRegression 으로 Boston_price 예측머신러닝(MACHINE LEARNING)/간단하게 이론(Theory...) 2021. 4. 22. 15:04
간단한 LinearRegression 으로 Boston_price 예측을 해보자. 1. 먼저 load boston 모델 load In [1]: from sklearn.datasets import load_boston import matplotlib.pyplot as plt import numpy as np In [2]: boston = load_boston() boston["data"] Out[2]: array([[6.3200e-03, 1.8000e+01, 2.3100e+00, ..., 1.5300e+01, 3.9690e+02, 4.9800e+00], [2.7310e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9690e+02, 9.1400e+00], [2.7290..
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Gradient_descent 으로 구현한 Linear_Regression머신러닝(MACHINE LEARNING)/간단하게 이론(Theory...) 2021. 4. 22. 14:17
https://www.boostcourse.org/ai222/lecture/24517 머신러닝을 위한 파이썬 부스트코스 무료 강의 www.boostcourse.org 네이버 부스트 코스 (Linear_Regression) 을 참고하였습니다. 1. 모듈 삽입 In [1]: import numpy as np import matplotlib.pyplot as plt %matplotlib inline import pandas as pd 2. LOAD DATASET 여기서, Load_excel()로 import In the following data X = number of claims Y = total payment for all the claims in thousands of Swedish Kronor for..