Python练习之——肿瘤预测

良/恶性肿瘤预测问题属于典型的二分类问题，本文采用LR分类器来预测未知肿瘤患者的分类。本次学习任务，训练数据集有524条数据，测试数据集有174条数据。
数据集信息如下所示：

1.读取数据集，采用LR（Logistic Regression）分类器学习，
计算出不同情况下的准确率，并可视化的展示出来。具体实现代码如下所示：

# -*- coding: utf-8 -*-"""Created on Fri Mar 17 10:38:46 2017@author: zch"""import numpy as npimport pandas as pdimport matplotlib.pyplot as pltfrom sklearn.linear_model import LogisticRegression#读取训练数据集df_train = pd.read_csv('E:\\Python\\kaggle_exercise\\Datasets\\Breast-Cancer\\breast-cancer-train.csv')#读取测试数据集df_test = pd.read_csv('E:\\Python\\kaggle_exercise\\Datasets\\Breast-Cancer\\breast-cancer-test.csv')#print(df_train.info())'''该数据总共有4列，除了编号之外，包含Clump Thickness，Cell Size，Type三个特征,其中前两个特征是数值型的，最后一项是布尔型，分别代表肿瘤患者的良性与恶性'''#选取'Clump Thickness'与'Cell Size'作为特征，构建测试集中的正负分类样本。df_test_negative = df_test.loc[df_test['Type'] == 0][['Clump Thickness','Cell Size']]df_test_positive = df_test.loc[df_test['Type'] == 1][['Clump Thickness','Cell Size']]#绘制良性肿瘤样本点，标记为红色的oplt.scatter(df_test_negative['Clump Thickness'],df_test_negative['Cell Size'],marker='o',s=200,c='red')#绘制恶性肿瘤样本点，标记为黑色的xplt.scatter(df_test_positive['Clump Thickness'],df_test_positive['Cell Size'],marker='x',s=150,c='black')#绘制x,y轴说明plt.xlabel('Clump Thickness')plt.ylabel('Cell Size')print(plt.show())#利用numpy中的random函数随机采样直线的截距和系数。intercept = np.random.random([1])coef = np.random.random([2])lx = np.arange(0,12)ly = (-intercept-lx * coef[0]) / coef[1]#绘制一条随机直线plt.plot(lx,ly,c='yellow')plt.scatter(df_test_negative['Clump Thickness'],df_test_negative['Cell Size'],marker='o',s=200,c='red')plt.scatter(df_test_positive['Clump Thickness'],df_test_positive['Cell Size'],marker='x',s=150,c='black')plt.xlabel('Clump Thickness')plt.ylabel('Cell Size')print(plt.show())#引入LR分类器lr = LogisticRegression()#使用前10条训练样本学习直线的系数和截距lr.fit(df_train[['Clump Thickness','Cell Size']][:10],df_train['Type'][:10])print("测试准确率：",lr.score(df_test[['Clump Thickness','Cell Size']],df_test['Type']))intercept = lr.intercept_coef = lr.coef_[0,:]ly = (-intercept - lx * coef[0]) / coef[1]plt.plot(lx,ly,c='green')plt.scatter(df_test_negative['Clump Thickness'],df_test_negative['Cell Size'],marker='o',s=200,c='red')plt.scatter(df_test_positive['Clump Thickness'],df_test_positive['Cell Size'],marker='x',s=150,c='black')plt.xlabel('Clump Thickness')plt.ylabel('Cell Size')print(plt.show())#使用所有训练样本学习直线的系数和截距lr.fit(df_train[['Clump Thickness','Cell Size']],df_train['Type'])print("测试准确率：",lr.score(df_test[['Clump Thickness','Cell Size']],df_test['Type']))plt.plot(lx,ly,c='blue')plt.scatter(df_test_negative['Clump Thickness'],df_test_negative['Cell Size'],marker='o',s=200,c='red')plt.scatter(df_test_positive['Clump Thickness'],df_test_positive['Cell Size'],marker='x',s=150,c='black')plt.xlabel('Clump Thickness')plt.ylabel('Cell Size')print(plt.show())

2.对比图3、图4，可以很明显的看出，同样采用LR分类器来学习，随着训练样本的增加，分类准确率得到了明显的提升。