김야키

데이터셋 1 : make_forge from mglearn 데이터셋 2 : load_breast_cancer from sklearn.datasets (유방암 데이터셋) 함수 : plot_feature_importances_cancer (해당 모델을 이용한 예측에서 feature들의 중요도 출력) In [1]: #-*- coding:utf-8 -*- %matplotlib inline import matplotlib.pyplot as plt from sklearn.linear_model import LogisticRegression from sklearn.svm import LinearSVC import mglearn x, y = mglearn.datasets.make_forge() fig, axes = p..

데이터셋 : ram_price.csv In [1]: import os import mglearn #-*- coding:utf-8 -*- %matplotlib inline import matplotlib.pyplot as plt import pandas as pd ram_prices = pd.read_csv(os.path.join(mglearn.datasets.DATA_PATH, "ram_price.csv")) plt.semilogy(ram_prices.date, ram_prices.price) plt.xlabel("Year") plt.ylabel("Price ($/Mbyte_") Out[1]: Text(0,0.5,'Price ($/Mbyte_') In [2]: from sklearn.tree import..