Python 数据科学 速查表 创建模型 Scikit-learn 呆鸟 译 有监督学习评估器 线性回归 >>> from sklearn.linear_model import LinearRegression >>> lr = LinearRegression(normalize=True) 天善智能 商业智能与大数据社区 www.hellobi.com 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) Scikit-learn 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。 简例 >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> 无监督学习评估器 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 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) 参阅 NumPy 与 Pandas 无监督学习 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 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 有监督学习 >>> lr.fit(X, y) >>> knn.fit(X_train, y_train) >>> svc.fit(X_train, y_train) >>> k_means.fit(X_train) >>> pca_model = pca.fit_transform(X_train) 拟合数据与模型 拟合数据与模型 拟合并转换数据 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 编码分类特征 from sklearn.preprocessing import StandardScaler scaler = StandardScaler().fit(X_train) standardized_X = scaler.transform(X_train) standardized_X_test = scaler.transform(X_test) >>> from sklearn.preprocessing import LabelEncoder >>> enc = LabelEncoder() >>> y = enc.fit_transform(y) >>> >>> >>> >>> from sklearn.preprocessing import Normalizer scaler = Normalizer().fit(X_train) normalized_X = scaler.transform(X_train) normalized_X_test = scaler.transform(X_test) >>> from sklearn.preprocessing import Imputer >>> imp = Imputer(missing_values=0, strategy='mean', axis=0) >>> imp.fit_transform(X_train) 二值化 >>> from sklearn.preprocessing import Binarizer >>> binarizer = Binarizer(threshold=0.0).fit(X) >>> binary_X = binarizer.transform(X) 输入缺失值 生成多项式特征 准确率 >>> knn.score(X_test, y_test) 评估器评分法 >>> from sklearn.metrics import accuracy_score 指标评分函数 >>> accuracy_score(y_test, y_pred) 分类预估评价函数 >>> from sklearn.metrics import classification_report 精确度、召回率、F1 >>> print(classification_report(y_test, y_pred)) 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) 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 >>> >>> >>> >>> 归一化 分类指标 群集指标 模型拟合 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。 >>> >>> >>> >>> 评估模型性能 >>> from sklearn.preprocessing import PolynomialFeatures >>> poly = PolynomialFeatures(5) >>> poly.fit_transform(X) >>> 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_) 原文作者 DataCamp Learn Python for Data Science Interactively