【八月长安tsliao】的博客

import pandas as pdimport numpy as npimport matplotlib.pyplot as pltfrom pandas import DataFrame, Seriesfrom sklearn.model_selection import train_test_splitfrom sklearn.linear_model import Linear...

# 数组模块import numpy as np# 存储了许多有用的数据集from sklearn import datasets# 分割数据的模块，把数据集分为训练集和测试集from sklearn.model_selection import train_test_split# KNN算法from sklearn.neighbors import KNeighborsClassi...

import pandas as pdimport numpy as np#导入数据集dataset = pd.read_csv('50_Startups.csv')X = dataset.iloc[ : , :-1].valuesY = dataset.iloc[ : , 4 ].values#将类别数据数字化from sklearn.preprocessing import...

import numpy as npimport matplotlib.pyplot as pltimport pandas as pd#导入数据dataset = pd.read_csv('Social_Network_Ads.csv')X = dataset.iloc[:, [2, 3]].valuesy = dataset.iloc[:, 4].values#拆分数据集为训...

import numpy as npimport matplotlib.pyplot as pltimport pandas as pd#导入数据dataset = pd.read_csv('Social_Network_Ads.csv')X = dataset.iloc[:, [2, 3]].valuesy = dataset.iloc[:, 4].values#将数据集拆分为...

经验误差与过拟合经验误差：学习器的实际预测输出与样本的真实输出之间的差异称为“误差”，学习器在训练集上的误差称为“训练误差”或“经验误差”。过拟合：当学习器把训练样本学的‘太好’的时候，很可能把训练样自身的一些特点当作所有潜在样本都会具有的一般性质，这样就会导致泛化性能力下降，这种现象在机器学习中称为“过拟合”。与过拟合相对的是欠拟合，这种是指对训练样本的一般性质尚未学好。评估方法留出法：...