原文：https://www.datacamp.com/community/blog/scikit-learn-cheat-sheet
Scikit-Learn相关资料：
Scikit-Learn介绍机器学习：http://scikit-learn.org/stable/tutorial/basic/tutorial.html
Scikit-Learn用户指南：http://scikit-learn.org/stable/user_guide.html
Scikit-Learn教程：http://scikit-learn.org/stable/tutorial/index.html
简介：
大多数正在使用Python学习数据科学的人肯定会听说过scikit-learn，这是一个开源的Python库，通过统一的界面，可以实现各种机器学习，预处理，交叉验证和可视化算法。
本文将向您介绍您需要经历的基本步骤，以成功实现机器学习算法：您将看到如何加载数据，如何对其进行预处理，如何创建您可以适合的模型您的数据和预测目标标签，如何验证您的模型以及如何进一步调整以提高其性能。
Scikit-learn是一个开源的Python库，使用统一的界面实现一系列机器学习，预处理，交叉验证和可视化算法。
实战
一个基本的例子
>>> 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)
加载数据
您的数据需要以数字形式存储为NumPy数组或SciPy稀疏矩阵。其他可转换为数字数组的类型，例如Pandas DataFrame也是可以接受的。>>> 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
预处理数据
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)
正常化
>>> 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)
伪造缺失值
>>>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)
>>> oly.fit_transform(X)
训练和测试数据
>>> from sklearn.model_selection import train_test_split>>> X_train, X_test, y_train, y_test = train_test_split(X,y,random_state=0)
创建你的模型
有监督学习估计器
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)
无监督学习估计
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
监督学习
>>> lr.fit(X, y)>>> knn.fit(X_train, y_train)
>>> svc.fit(X_train, y_train)
无监督学习
>>> k_means.fit(X_train)>>> pca_model = pca.fit_transform(X_train)
预测
监督估计量
>>> y_pred = svc.predict(np.random.random((2,5)))>>> y_pred = lr.predict(X_test)
>>> y_pred = knn.predict_proba(X_test))
无监督估计
>>> y_pred = k_means.predict(X_test)评估你的模型的性能
分类度量
精度分数
>>> knn.score(X_test, y_test)>>> from sklearn.metrics import accuracy_score
>>> accuracy_score(y_test, y_pred)
分类报告
>>> from sklearn.metrics import classification_report>>> print(classification_report(y_test, y_pred)))
混淆矩阵
>>> from sklearn.metrics import confusion_matrix>>> print(confusion_matrix(y_test, y_pred)))
回归度量
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))
R^ 2分数
>>> from sklearn.metrics import r2_score>>> r2_score(y_true, y_pred))
聚类度量
调整后的兰特指数
>>> 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
>>> print(cross_val_score(knn, X_train, y_train, cv=4))>>> print(cross_val_score(lr, X, y, cv=2))
调整你的模型
网格搜索
>>> 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_)