python机器学习

准备数据

import numpy as npimport urllib.request"""    下载数据    dataset前2行数据    >>> dataset[:2]    array([[   6.   ,  148.   ,   72.   ,   35.   ,    0.   ,   33.6  ,           0.627,   50.   ,    1.   ],       [   1.   ,   85.   ,   66.   ,   29.   ,    0.   ,   26.6  ,           0.351,   31.   ,    0.   ]])    >>> """url = """http://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data"""raw_data = urllib.request.urlopen(url)# 数据集dataset = np.loadtxt(raw_data , delimiter=",")# 输出数据前两行作为范例print("数据前2行：")print(dataset[:2])X = dataset[:,:8]Y = dataset[:,8]print("=================================")

logistic回归模型

"""    logistic 回归模型"""from sklearn import metricsfrom sklearn.linear_model import LogisticRegressionmodel = LogisticRegression()# 训练模型model.fit(X,Y)expected = Ypredicted = model.predict(X)print("罗基斯特回归模型：")print(model)print()print(metrics.classification_report(expected, predicted))# 混淆矩阵print(metrics.confusion_matrix(expected, predicted))print()

输出：

罗基斯特回归模型：LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,          verbose=0, warm_start=False)             precision    recall  f1-score   support        0.0       0.79      0.90      0.84       500        1.0       0.74      0.55      0.63       268avg / total       0.77      0.77      0.77       768[[448  52] [121 147]]

朴素贝叶斯分类模型

"""    朴素贝叶斯分类模型"""from sklearn import metricsfrom sklearn.naive_bayes import GaussianNBmodel = GaussianNB()model.fit(X,Y)expected = Ypredicted = model.predict(X)print("朴素贝叶斯分类模型：")print(model)print()print(metrics.classification_report(expected, predicted))# 混淆矩阵print(metrics.confusion_matrix(expected, predicted))print()

输出：

朴素贝叶斯分类模型：GaussianNB(priors=None)             precision    recall  f1-score   support        0.0       0.80      0.84      0.82       500        1.0       0.68      0.62      0.64       268avg / total       0.76      0.76      0.76       768[[421  79] [103 165]]

KNN算法

"""    K-最邻近"""from sklearn import metricsfrom sklearn.neighbors import KNeighborsClassifiermodel = KNeighborsClassifier()model.fit(X,Y)expected = Ypredicted = model.predict(X)print("K-最邻近：")print(model)print()print(metrics.classification_report(expected, predicted))# 混淆矩阵print(metrics.confusion_matrix(expected, predicted))print()

输出：

K-最邻近：KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',           metric_params=None, n_jobs=1, n_neighbors=5, p=2,           weights='uniform')             precision    recall  f1-score   support        0.0       0.83      0.88      0.85       500        1.0       0.75      0.65      0.70       268avg / total       0.80      0.80      0.80       768[[442  58] [ 93 175]]

决策树模型

"""    决策树"""from sklearn import metricsfrom sklearn.tree import DecisionTreeClassifiermodel = DecisionTreeClassifier()model.fit(X,Y)expected = Ypredicted = model.predict(X)print("决策树：")print(model)print()print(metrics.classification_report(expected, predicted))# 混淆矩阵print(metrics.confusion_matrix(expected, predicted))

输出

决策树：DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,            max_features=None, max_leaf_nodes=None,            min_impurity_decrease=0.0, min_impurity_split=None,            min_samples_leaf=1, min_samples_split=2,            min_weight_fraction_leaf=0.0, presort=False, random_state=None,            splitter='best')             precision    recall  f1-score   support        0.0       1.00      1.00      1.00       500        1.0       1.00      1.00      1.00       268avg / total       1.00      1.00      1.00       768[[500   0] [  0 268]]

SVC

"""    支持向量机器"""from sklearn import metricsfrom sklearn.svm import SVCmodel = SVC()model.fit(X,Y)expected = Ypredicted = model.predict(X)print("SVC：")print(model)print()print(metrics.classification_report(expected, predicted))# 混淆矩阵print(metrics.confusion_matrix(expected, predicted))

输出：

SVC：SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',  max_iter=-1, probability=False, random_state=None, shrinking=True,  tol=0.001, verbose=False)             precision    recall  f1-score   support        0.0       1.00      1.00      1.00       500        1.0       1.00      1.00      1.00       268avg / total       1.00      1.00      1.00       768[[500   0] [  0 268]]