考核成绩分类

1、Logistic RegressionThe data我们将建立一个逻辑回归模型来预测一个学生是否被大学录取。假设你是一个大学系的管理 员，你想根据两次考试的结果来决定每个申请人的录取机会。你有以前的申请人的历史数据， 你可以用它作为逻辑回归的训练集。对于每一个培训例子，你有两个考试的申请人的分数和 录取决定。为了做到这一点，我们将建立一个分类模型，根据考试成绩估计入学概率。#三大件import numpy as npimport pandas as pdimport matplotlib.pyplot as plt%matplotlib inlineimport os#path = data

2、 + os.sep + LogiReg_data.txtpdData = pd.read_csv(F:W究课程机器学习宇迪系列python数据分析与机器学习实战- 全-E唐宇迪-机器学习课程资料机器学习算法配套案例实战梯度下降 dataWLogiReg_data.txt, header=None, names=Exam 1, Exam 2, Admitted)pdData.head()pdData.shapepositive = pdDatapdDataAdmitted = 1 # returns the subset of rows such Admitted = 1, i.e.the se

3、t of positive* examplesnegative = pdDatapdDataAdmitted = 0 # returns the subset of rows such Admitted = 0, i.e.the set of negative* examplesfig, ax = plt.subplots(figsize=(10,5)ax.scatter(positiveExam 1, positiveExam 2, s=30, c=b, marker=o, label=Admitted) ax.scatter(negativeExam 1, negativeExam 2,

4、s=30, c=r, marker=x, label=Not Admitted) ax.legend()ax.set_xlabel(Exam 1 Score)ax.set_ylabel(Exam 2 Score)rflrtplotlibB1;errt, Tt at ftdtdjg】艮rflrtplotlibB1;errt, Tt at ftdtdjg】艮U pdb.set_trace();theta, iter, costs, grad, dur = descent(data, theta, batchSize, stopType, thresh, alpha) name = Original

5、 if (data:,12).sum() 1 else Scaled name += data - learning rate: ( - .format(alpha)if batchSize=n: strDescType = Gradientelif batchSize=1: strDescType = Stochasticelse: strDescType = Mini-batch ().format(batchSize) name += strDescType + descent - Stop:if stopType = STOP_ITER: strStop = iterations.fo

6、rmat(thresh)elif stopType = STOP_COST: strStop = costs change .format(thresh) else: strStop = gradient norm .format(thresh)name += strStopprint (*nTheta: - Iter: - Last cost: :03.2f - Duration: :03.2fs.format( name, theta, iter, costs-1, dur)fig, ax = plt.subplots(figsize=(12,4)ax.plot(np.arange(len

7、(costs), costs, r)ax.set_xlabel(Iterations)ax.set_ylabel(Cost)ax.set_title(name.upper() + - Error vs. Iteration) return theta不同的停止策略设定迭代次数#选择的梯度下降方法是基于所有样本的n=100runExpe(orig_data, theta, n, STOP_ITER, thresh=5000, alpha=0.000001)OCB0.65data. leamon： istt. le-Ofi - Gradient drecent OCB0.65data. leamo

8、n： istt. le-Ofi - Gradient drecent - Slap: ECCO Lt edtimuTheta: -D.DDD27J27 0. DtHDEEZ . 003761 - Ptei 5000 - Last cort D. 63 - Duration L. LBsarray f -Cl OT27127.H OJT既32, 0. OD37bT L J4WD5DD01D0O3000RzcrationsORIGINAL DATA - LEARNING RAT巳】E-脆-GRADIENT DESCENT * STOP: 5000 ITERATIONS Error vs. Iter

9、ation根据损失值停止设定阈值1E-6,差不多需要110 000次迭代根据梯度变化停止设定阈值0.05，差不多需要40 000次迭代runExpe(orig_data, theta, n, STOP_GRAD, thresh=0.05, alpha=0.001)*0 rigina.1 data - 1 eztrning rate: . DO - Gr ajdi mt ifeicent - Stop gradisit noni -05Ib&ti -盅务CBMCig il 0272692 LL0L&39O - It&r: 0015 - Lat cost. Hd - Durstim HI T9sm

10、J -l.；：；ELO?. 0.: ： DD1 - Jtm-bat th (L6) dcrcEDt - Stop. 1ECCO Lt-Eatiims- Thfita -1.0352224 Q, 口16龄的 D.ni&IS4 - Tter: 150M - L笙t s亦 0.57 - Dijratim- L44s匹工wU T35座24, o. oiettesn8. o. 012 )涅 FUQMAL 口AT A - LEARNING RATE: U,tXJl - MlMl-BRTCHlW) 口E$CEN- STOP: 150Qd ITERATION 三-Errw g. ItwrHimQ2QDU400

11、06DODBOCDLODaCL2ODOUODOLHaO浮动仍然比较大，我们来尝试下对数据进行标准化将数据按其属性（按列进行）减去其均值， 然后除以其方差。最后得到的结果是，对每个属性/每列来说所有数据都聚集在0附近，方 差值为1from sklearn import preprocessing as pp scaled_data = orig_data.copy()scaled_data:, 1:3 = pp.scale(orig_data:, 1:3) runExpe(scaled_data, theta, n, STOP_ITER, thresh=5000, alpha=0.001)它好多了！原始数据，只能达到达到0.61，而我们得到了 0.38个在这里！所以对数据做预 处理是非常重要的精度#设定阈值def predict(X, theta):return 1 if x = 0.5 else 0 for x in model(X, theta)scaled_X = scaled_data:, :3y = scaled_data:, 3predictions = predict(scaled_X, th