Scikit-Learn 是开源的 Python 库，通过统一的界面实现机器学习、预处理、交叉验证及可视化算法。

一个简单的例子                    

from sklearn import neighbors, datasets, preprocessing
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
iris = datasets.load_iris()
X, y = iris.data[:, :2], iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33)
scaler = preprocessing.StandardScaler().fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
knn = neighbors.KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)
accuracy_score(y_test, y_pred)                               

使用过程

加载数据

Scikit-learn 处理的数据是存储为 NumPy 数组或 SciPy 稀疏矩阵的 数字，还支持 Pandas 数据框等可转换为数字数组的其它数据类型。

import numpy as np
X = np.random.random((10,5))
y = np.array(['M','M','F','F','M','F','M','M','F','F','F'])
X[X < 0.7] = 0

训练集与测试集数据

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

数据预处理

• 标准化

  from sklearn.preprocessing import StandardScaler
  scaler = StandardScaler().fit(X_train)
  standardized_X = scaler.transform(X_train)
  standardized_X_test = scaler.transform(X_test)
  

• 归一化

  from sklearn.preprocessing import Normalizer
  scaler = Normalizer().fit(X_train)
  normalized_X = scaler.transform(X_train)
  normalized_X_test = scaler.transform(X_test)
  

• 二值化

  from sklearn.preprocessing import Binarizer
  binarizer = Binarizer(threshold=0.0).fit(X)
  binary_X = binarizer.transform(X)
  

• 编码分类特征

  from sklearn.preprocessing import LabelEncoder
  enc = LabelEncoder()
  y = enc.fit_transform(y)
  

• 输入缺失值

  from sklearn.preprocessing import Imputer
  imp = Imputer(missing_values=0, strategy='mean', axis=0)
  imp.fit_transform(X_train)
  

• 生成多项式特征

  from sklearn.preprocessing import PolynomialFeatures
  poly = PolynomialFeatures(5)
  poly.fit_transform(X)
  

创建模型

• 有监督学习评估器
  • 线性回归

      from sklearn.linear_model import LinearRegression
      lr = LinearRegression(normalize=True)
    

  • 支持向量机(SVM)

      from sklearn.svm import SVC
      svc = SVC(kernel='linear')
    

  • 朴素贝叶斯

      from sklearn.naive_bayes import GaussianNB
      gnb = GaussianNB()
    

  • KNN

      from sklearn import neighbors
      knn = neighbors.KNeighborsClassifier(n_neighbors=5)
    

• 无监督学习评估器
  • 主成分分析(PCA)

      from sklearn.decomposition import PCA
      pca = PCA(n_components=0.95)
    

  • K-Means

      from sklearn.cluster import KMeans
      k_means = KMeans(n_clusters=3, random_state=0)
    

模型拟合

• 有监督学习

  lr.fit(X, y)                           # 拟合数据与模型
  knn.fit(X_train, y_train)
  svc.fit(X_train, y_train)
  

• 无监督学习

  k_means.fit(X_train)                   # 拟合数据与模型
  pca_model = pca.fit_transform(X_train) # 拟合并转换数据
  

模型预测

• 有监督评估器

  y_pred = svc.predict(np.random.random((2,5)))# 预测标签
  y_pred = lr.predict(X_test)           # 预测标签
  y_pred = knn.predict_proba(X_test)    # 评估标签概率
  

• 无监督评估器

  y_pred = k_means.predict(X_test)      # 预测聚类算法里的标签
  

评估模型性能

• 分类指标
  • 准确率

      knn.score(X_test, y_test)             # 评估器评分法
      from sklearn.metrics import accuracy_score
      accuracy_score(y_test, y_pred)        # 指标评分函数
    

  • 分类预估评价函数

      from sklearn.metrics import classification_report
      print(classification_report(y_test, y_pred))   # 精确度、召回率、F1分数及支持率
    

  • 混淆矩阵

      from sklearn.metrics import confusion_matrix
      print(confusion_matrix(y_test, y_pred))
    

• 回归指标
  • 平均绝对误差

      from sklearn.metrics import       
      mean_absolute_error y_true = [3, -0.5, 2]
      mean_absolute_error(y_true, y_pred)
    

  • 均方误差

      from sklearn.metrics import mean_squared_error
      mean_squared_error(y_test, y_pred)
    

  • R² 评分

      from sklearn.metrics import r2_score
      r2_score(y_true, y_pred)
    

• 群集指标
  • 调整兰德系数

      from sklearn.metrics import adjusted_rand_score
      adjusted_rand_score(y_true, y_pred)
    

  • 同质性

      from sklearn.metrics import homogeneity_score
      homogeneity_score(y_true, y_pred)
    

  • V-measure

      from sklearn.metrics import v_measure_score
      metrics.v_measure_score(y_true, y_pred)
    

• 交叉验证

  from sklearn.cross_validation import cross_val_score
  print(cross_val_score(knn, X_train, y_train, cv=4))
  print(cross_val_score(lr, X, y, cv=2))
  

模型调整

• 栅格搜索

  from sklearn.grid_search import GridSearchCV
  params = {"n_neighbors": np.arange(1,3),
        "metric": ["euclidean", "cityblock"]}
  grid = GridSearchCV(estimator=knn,
        param_grid=params)
  grid.fit(X_train, y_train)
  print(grid.best_score_)
  print(grid.best_estimator_.n_neighbors)
  

• 随机参数优化

  from sklearn.grid_search import RandomizedSearchCV
  params = {"n_neighbors": range(1,5), 
        "weights": ["uniform", "distance"]}
  rsearch = RandomizedSearchCV(estimator=knn,
        param_distributions=params,
        cv=4,
        n_iter=8,
        random_state=5)
  rsearch.fit(X_train, y_train)
  print(rsearch.best_score_)
  

内容来源：DataCamp


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