机器学习——监督分类小项目（识别作者）

浅浅的花香味﹌ 2024-04-19 12:40 42阅读 0赞

### 文章目录 ###

*  朴素贝叶斯小项目——识别作者
 *  SVM小项目——识别作者
 *  决策树小项目——识别作者
 *  KNN小项目——识别作者
 *  Adaboost小项目——识别作者
 *  RandomForest小项目——识别作者

整理了六种监督算法在同一项目上实现的方法。

# 朴素贝叶斯小项目——识别作者 #

参见文章 [机器学习（1）——贝叶斯网络分类算法][1] 最后一部分

# SVM小项目——识别作者 #

参见 文章 [机器学习——SVM][SVM]

# 决策树小项目——识别作者 #

参见 文章 [机器学习——决策树][Link 1]

# KNN小项目——识别作者 #

#!/usr/bin/python
    
    import matplotlib.pyplot as plt
    from prep_terrain_data import makeTerrainData
    from class_vis import prettyPicture
    
    features_train, labels_train, features_test, labels_test = makeTerrainData()
    
    
    ### the training data (features_train, labels_train) have both "fast" and "slow"
    ### points mixed together--separate them so we can give them different colors
    ### in the scatterplot and identify them visually
    grade_fast = [features_train[ii][0] for ii in range(0, len(features_train)) if labels_train[ii]==0]
    bumpy_fast = [features_train[ii][1] for ii in range(0, len(features_train)) if labels_train[ii]==0]
    grade_slow = [features_train[ii][0] for ii in range(0, len(features_train)) if labels_train[ii]==1]
    bumpy_slow = [features_train[ii][1] for ii in range(0, len(features_train)) if labels_train[ii]==1]
    
    
    #### initial visualization
    plt.xlim(0.0, 1.0)
    plt.ylim(0.0, 1.0)
    plt.scatter(bumpy_fast, grade_fast, color = "b", label="fast")
    plt.scatter(grade_slow, bumpy_slow, color = "r", label="slow")
    plt.legend()
    plt.xlabel("bumpiness")
    plt.ylabel("grade")
    plt.show()
    ################################################################################
    
    
    
    from time import time
    from sklearn.neighbors import KNeighborsClassifier
    
    clf = KNeighborsClassifier()
    
    t0 = time()
    clf.fit(features_train, labels_train)
    print "training time:", round(time()-t0, 3), "s"
    t1 = time()
    
    pred = clf.predict(features_test)
    print "testing time:", round(time()-t1, 3), "s"
    
    from sklearn.metrics import accuracy_score
    
    acc = accuracy_score(pred, labels_test)
    print acc
    
    
    
    
    try:
        prettyPicture(clf, features_test, labels_test)
    except NameError:
        pass

training time: 0.164 s  
testing time: 0.012 s  
0.92

# Adaboost小项目——识别作者 #

#!/usr/bin/python
    
    import matplotlib.pyplot as plt
    from prep_terrain_data import makeTerrainData
    from class_vis import prettyPicture
    
    features_train, labels_train, features_test, labels_test = makeTerrainData()
    
    
    ### the training data (features_train, labels_train) have both "fast" and "slow"
    ### points mixed together--separate them so we can give them different colors
    ### in the scatterplot and identify them visually
    grade_fast = [features_train[ii][0] for ii in range(0, len(features_train)) if labels_train[ii]==0]
    bumpy_fast = [features_train[ii][1] for ii in range(0, len(features_train)) if labels_train[ii]==0]
    grade_slow = [features_train[ii][0] for ii in range(0, len(features_train)) if labels_train[ii]==1]
    bumpy_slow = [features_train[ii][1] for ii in range(0, len(features_train)) if labels_train[ii]==1]
    
    
    #### initial visualization
    plt.xlim(0.0, 1.0)
    plt.ylim(0.0, 1.0)
    plt.scatter(bumpy_fast, grade_fast, color = "b", label="fast")
    plt.scatter(grade_slow, bumpy_slow, color = "r", label="slow")
    plt.legend()
    plt.xlabel("bumpiness")
    plt.ylabel("grade")
    plt.show()
    ################################################################################
    
    
    from time import time
    from sklearn.ensemble import AdaBoostClassifier
    
    clf = AdaBoostClassifier()
    
    t0 = time()
    clf.fit(features_train, labels_train)
    print "adaboost training time:", round(time()-t0, 3), "s"
    t1 = time()
    
    pred = clf.predict(features_test)
    print "adaboost testing time:", round(time()-t1, 3), "s"
    
    from sklearn.metrics import accuracy_score
    
    acc = accuracy_score(pred, labels_test)
    print "adaboost accuracy: ", acc
    
    
    
    try:
        prettyPicture(clf, features_test, labels_test)
    except NameError:
        pass

adaboost training time: 0.196 s  
adaboost testing time: 0.019 s  
adaboost accuracy: 0.924

# RandomForest小项目——识别作者 #

#!/usr/bin/python
    
    import matplotlib.pyplot as plt
    from prep_terrain_data import makeTerrainData
    from class_vis import prettyPicture
    
    features_train, labels_train, features_test, labels_test = makeTerrainData()
    
    
    ### the training data (features_train, labels_train) have both "fast" and "slow"
    ### points mixed together--separate them so we can give them different colors
    ### in the scatterplot and identify them visually
    grade_fast = [features_train[ii][0] for ii in range(0, len(features_train)) if labels_train[ii]==0]
    bumpy_fast = [features_train[ii][1] for ii in range(0, len(features_train)) if labels_train[ii]==0]
    grade_slow = [features_train[ii][0] for ii in range(0, len(features_train)) if labels_train[ii]==1]
    bumpy_slow = [features_train[ii][1] for ii in range(0, len(features_train)) if labels_train[ii]==1]
    
    
    #### initial visualization
    plt.xlim(0.0, 1.0)
    plt.ylim(0.0, 1.0)
    plt.scatter(bumpy_fast, grade_fast, color = "b", label="fast")
    plt.scatter(grade_slow, bumpy_slow, color = "r", label="slow")
    plt.legend()
    plt.xlabel("bumpiness")
    plt.ylabel("grade")
    plt.show()
    ################################################################################
    
    
    
    
    from time import time
    from sklearn.ensemble import RandomForestClassifier
    import math
    
    n_features = len(features_train[0])
    
    clf = RandomForestClassifier(n_estimators=1)
    
    t0 = time()
    clf.fit(features_train, labels_train)
    print "RandomForest training time:", round(time()-t0, 3), "s"
    t1 = time()
    
    pred = clf.predict(features_test)
    print "RandomForest testing time:", round(time()-t1, 3), "s"
    
    from sklearn.metrics import accuracy_score
    
    acc = accuracy_score(pred, labels_test)
    print "RandomForest accuracy: ", acc
    
    
    try:
        prettyPicture(clf, features_test, labels_test)
    except NameError:
        pass

RandomForest training time: 0.04 s  
RandomForest testing time: 0.009 s  
RandomForest accuracy: 0.92

[1]: https://blog.csdn.net/xiao_lxl/article/details/40786809
[SVM]: https://blog.csdn.net/xiao_lxl/article/details/97148251
[Link 1]: https://blog.csdn.net/xiao_lxl/article/details/96317941