sklearn使用numpy ndarray或者pandas dataframe作為訓練數據，調用fit()函數即可完成訓練。
本部分我們先介紹一下sklearn的基本用法。

二分類

我們先看一個二分類問題，將mnist分類成數字5和非5兩類：

from sklearn.datasets import fetch_openml mnist = fetch_openml('mnist_784', version=1) X,y = mnist['data'], mnist['target']X_train, X_test = X[:6000], X[6000:] y_train, y_test = y[:6000].astype(np.uint8), y[6000:].astype(np.uint8) y_train_5 = (y_train == 5) y_test_5 = (y_test == 5)from sklearn.linear_model import SGDClassifier sgd_clf = SGDClassifier(loss='hinge') sgd_clf.fit(X_train, y_train_5) print(sgd_clf.predict([X[0]]))from sklearn.model_selection import cross_val_score cross_val_score(sgd_model, X_train, y_train_5, cv=3, scoring='accuracy') [False]array([0.96 , 0.9575, 0.964 ])

回歸

我們再看一個回歸算法的示例，使用的是housing數據集，預測地區房產的中位數。

DOWNLOAD_ROOT = "https://raw.githubusercontent.com/ageron/handson-ml2/master/" HOUSING_PATH = os.path.join("datasets", "housing") HOUSING_URL = DOWNLOAD_ROOT + "datasets/housing/housing.tgz"def fetch_housing_data(housing_url=HOUSING_URL, housing_path=HOUSING_PATH):if not os.path.isdir(housing_path):os.makedirs(housing_path)tgz_file = os.path.join(housing_path,'housing.tgz')urllib.request.urlretrieve(housing_url, tgz_file)housing_tgz = tarfile.open(tgz_file)housing_tgz.extractall(path = housing_path) #解壓文件housing_tgz.close()# fetch_housing_data()housing = pd.read_csv(os.path.join(HOUSING_PATH,'housing.csv'))median = housing['total_bedrooms'].median() housing['total_bedrooms'].fillna(median,inplace=True)housing_label = housing['median_house_value'] housing_feature = housing.drop(['median_house_value','ocean_proximity'], axis=1)from sklearn.linear_model import LinearRegression lin_reg = LinearRegression() lin_reg.fit(housing_feature,housing_label) print(model.intercept_, model.coef_) -3570118.0614940603 [-4.26104026e+04 -4.24754782e+04 1.14445085e+03 -6.62091740e+008.11609666e+01 -3.98732002e+01 7.93047225e+01 3.97522237e+04]

總結

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