机器学习笔记

这个笔记是我个人在学习机器学习过程中的学习记录。其中有大量前人的总结成果，但此词条并不简单的重复 摘抄，而是我在学习中选择性地参考了不同资料的不同部分，自己学习的理解与总结。原词条都有相关链接， 请自行跳转。

• udacity数据挖掘工程师直通班
• Python学习笔记
• Python Pandas库笔记
• Python Numpy库笔记
• Pytorch笔记
• 《机器学习实战》学习笔记
• sklearn学习笔记
• 维基百科 机器学习笔记

一些其他有用的东西：

• 数据科学领域速查表
• 文献阅读总结：有监督的二分类学习算法的经验比较
• 文献阅读总结：Fern´andez-Delgado2014. 对比了17种分类算法的197种实现，最优秀的是随机森林。
• 这里比较了sklearn的不同分类器 代码如下：

  print(__doc__)
  
  
  # Code source: Gaël Varoquaux
  #              Andreas Müller
  # Modified for documentation by Jaques Grobler
  # License: BSD 3 clause
  
  import numpy as np
  import matplotlib.pyplot as plt
  from matplotlib.colors import ListedColormap
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import StandardScaler
  from sklearn.datasets import make_moons, make_circles, make_classification
  from sklearn.neural_network import MLPClassifier
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.svm import SVC
  from sklearn.gaussian_process import GaussianProcessClassifier
  from sklearn.gaussian_process.kernels import RBF
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
  from sklearn.naive_bayes import GaussianNB
  from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
  
  help(SVC.score)
  exit(0)
  
  h = .02  # step size in the mesh
  
  names = ["Nearest Neighbors", "Linear SVM", "RBF SVM", "Gaussian Process",
           "Decision Tree", "Random Forest", "Neural Net", "AdaBoost",
           "Naive Bayes", "QDA"]
  
  classifiers = [
      KNeighborsClassifier(3),
      SVC(kernel="linear", C=0.025),
      SVC(gamma=2, C=1),
      GaussianProcessClassifier(1.0 * RBF(1.0)),
      DecisionTreeClassifier(max_depth=5),
      RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),
      MLPClassifier(alpha=1, max_iter=1000),
      AdaBoostClassifier(),
      GaussianNB(),
      QuadraticDiscriminantAnalysis()]
  
  X, y = make_classification(n_features=2, n_redundant=0, n_informative=2,
                             random_state=1, n_clusters_per_class=1)
  rng = np.random.RandomState(2)
  X += 2 * rng.uniform(size=X.shape)
  linearly_separable = (X, y)
  
  datasets = [make_moons(noise=0.3, random_state=0),
              make_circles(noise=0.2, factor=0.5, random_state=1),
              linearly_separable
              ]
  
  figure = plt.figure(figsize=(27, 9))
  i = 1
  # iterate over datasets
  for ds_cnt, ds in enumerate(datasets):
      # preprocess dataset, split into training and test part
      X, y = ds
      X = StandardScaler().fit_transform(X)
      X_train, X_test, y_train, y_test = \
          train_test_split(X, y, test_size=.4, random_state=42)
  
      x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
      y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
      xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                           np.arange(y_min, y_max, h))
  
      # just plot the dataset first
      cm = plt.cm.RdBu
      cm_bright = ListedColormap(['#FF0000', '#0000FF'])
      ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
      if ds_cnt == 0:
          ax.set_title("Input data")
      # Plot the training points
      ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright,
                 edgecolors='k')
      # Plot the testing points
      ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6,
                 edgecolors='k')
      ax.set_xlim(xx.min(), xx.max())
      ax.set_ylim(yy.min(), yy.max())
      ax.set_xticks(())
      ax.set_yticks(())
      i += 1
  
      # iterate over classifiers
      for name, clf in zip(names, classifiers):
          ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
          clf.fit(X_train, y_train)
          score = clf.score(X_test, y_test)
  
          # Plot the decision boundary. For that, we will assign a color to each
          # point in the mesh [x_min, x_max]x[y_min, y_max].
          if hasattr(clf, "decision_function"):
              Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
          else:
              Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]
  
          # Put the result into a color plot
          Z = Z.reshape(xx.shape)
          ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)
  
          # Plot the training points
          ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright,
                     edgecolors='k')
          # Plot the testing points
          ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright,
                     edgecolors='k', alpha=0.6)
  
          ax.set_xlim(xx.min(), xx.max())
          ax.set_ylim(yy.min(), yy.max())
          ax.set_xticks(())
          ax.set_yticks(())
          if ds_cnt == 0:
              ax.set_title(name)
          ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'),
                  size=15, horizontalalignment='right')
          i += 1
  
  plt.tight_layout()
  plt.show()