코딩걸음마

1. Data 불러오기 import pandas as pd import seaborn as sns import matplotlib.pyplot as plt 로지스틱회귀에 사용할 sample은 유명한 유방암 예측입니다. from sklearn.datasets import load_breast_cancer cancer = load_breast_cancer() print(cancer.DESCR) 2. Data 확인하기 .. _breast_cancer_dataset: Breast cancer wisconsin (diagnostic) dataset -------------------------------------------- **Data Set Characteristics:** :Number of Instanc..

1. Data 불러오기 !pip install matplotlib seaborn pandas sklearn import pandas as pd import seaborn as sns import matplotlib.pyplot as plt 선형회귀에 사용할 sample은 유명한 당뇨병 예측입니다. 원래는 분류식으로 활용을 해야 정확하지만, 회귀식으로 dataset을 사용해보겠습니다. from sklearn.datasets import load_diabetes diabetes = load_diabetes() print(diabetes.DESCR) 2. Data 확인하기 .. _diabetes_dataset: Diabetes dataset ---------------- Ten baseline variable..

import torch def mse(x_hat, x): # |x_hat| = (batch_size, dim) # |x| = (batch_size, dim) y = ((x - x_hat)**2).mean() return y x = torch.FloatTensor([[1, 1], [2, 2]]) x_hat = torch.FloatTensor([[0, 0], [0, 0]]) print(x.size(), x_hat.size()) torch.Size([2, 2]) torch.Size([2, 2]) mse(x_hat, x) tensor(2.5000) MSE in PyTorch import torch.nn.functional as F F.mse_loss(x_hat, x) tensor(2.5000) reduction..

1. Data 불러오기 !pip install matplotlib seaborn pandas sklearn import pandas as pd import seaborn as sns import matplotlib.pyplot as plt 선형회귀에 사용할 sample은 유명한 boston 주택가격 예측입니다. from sklearn.datasets import load_boston boston = load_boston() print(boston.DESCR) 2. Data 확인하기 .. _boston_dataset: Boston house prices dataset --------------------------- **Data Set Characteristics:** :Number of Instances..