1번

import numpy as np
arr1 = np.arange(1, 13).reshape(3,4)
arr2 = np.arange(13, 25).reshape(3,4)
arr3 = np.append(arr1, arr2, axis = 1)
print(arr3)

2번

정답 : 3,5

3번 일반적으로는 underfitting. 잘못 섞으면 overfitting도 발생가능.

4번은 애매하다.

cf) 5번

3번

from sklearn.datasets import load_wine
wine = load_wine()
import pandas as pd
import matplotlib.pyplot as plt
wine_df = pd.DataFrame(wine.data, columns = wine.feature_names)
plt.scatter(wine_df['alcohol'], wine_df['malic_acid'],color='red')
plt.xlabel('Alcohol')
plt.ylabel('Malic acid')
plt.show()
plt.scatter(wine_df['ash'], wine_df['alcalinity_of_ash'],color='blue')
plt.xlabel('Ash')
plt.ylabel('Alcalinity of ash')
plt.show()

4번

from sklearn.preprocessing import StandardScaler # return scaler에 넣으려면 dataframe으로 해야함 
scaler = StandardScaler()
scaler.fit(wine_df)
wine_scaled = scaler.transform(wine_df)
df_scaled_wine = pd.DataFrame(wine_scaled, columns= wine.feature_names)
print(df_scaled_wine.mean())
print(df_scaled_wine.std())

plt.scatter(df_scaled_wine['alcohol'], df_scaled_wine['malic_acid'])
plt.xlabel('Alcohol')
plt.ylabel('Malic acid')
plt.show()

plt.scatter(df_scaled_wine['ash'], df_scaled_wine['alcalinity_of_ash'])
plt.xlabel('Ash')
plt.ylabel('Alcalinity of ash')
plt.show()

5번