분류 전체보기

import mathimport numpy as npfrom sklearn.datasets import load_irisimport matplotlib.pyplot as plt iris = load_iris()print(iris.keys())print(iris.DESCR) dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename']) ...중요한 정보만 확인 :Attribute Information: - sepal length in cm - sepal width in cm - petal length in cm - petal width in cm - class: - Iris-Setosa - Iris-Versicolour..

import mathimport matplotlib.pyplot as pltimport numpy as np def odds(p): """성공확률 / 실패확률""" return p / (1 - p) def log_odds(p): """odds에 log를 취한 값""" return math.log(odds(p)) def sigmoid(t): """logistic: log_odds(odds에 log를 취한 값)을 알고 있을 때, 성공 확률 p를 계산""" return 1 / (1 + math.exp(-t)) p = 0.8print(f'p = {p}, odds(p) = {odds(p)}, log_odds(p) = {log_odds(p)}')p = 0.8, odds(p) = 4.000000000000001, l..

from sklearn import datasetsimport pandas as pdimport seaborn as snsimport matplotlib.pyplot as plt iris = datasets.load_iris()X = iris['data'] # iris.datay = iris['target'] # iris.targetfeatures = iris['feature_names'] # iris.feature_namesprint(features)['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)'] iris_df = pd.DataFrame(X,columns=['sepal_length', 'sepal_wid..

import seaborn as snsimport matplotlib.pyplot as pltfrom sklearn.datasets import load_bostonimport pandas as pd boston = load_boston()X = boston['data'] # boston.datay = boston['target'] # boston.targetfeatures = boston['feature_names'] # boston.feature_names # DataFrame으로 변환boston_df = pd.DataFrame(X, columns = features, index= None) boston_df['Price'] = yprint(boston_df.head())print(boston_df...