from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn import datasets #导入数据 iris = datasets.load_iris() #区分数据的自变量和因变量 iris_X = iris.data iris_Y = iris.target #将数据分成训练集和测试集，比例为：80%和20% iris_train_X , iris_test_X, iris_train_Y ,iris_test_Y = train_test_split( iris_X, iris_Y, test_size=0.2,random_state=0) #训练逻辑回归模型 log_reg = LogisticRegression() #此处这个函数中有很多参数可供选择 log_reg.fit(iris_train_X, iris_train_Y) #预测 predict = log_reg.predict(iris_test_X) accuracy = log_reg.score(iris_test_X,iris_test_Y)
from sklearn.datasets import load_boston from sklearn.linear_model import LinearRegression X, y = load_boston(return_X_y=True) print(X.shape) model = LinearRegression() model.fit(X, y) print(model.predict(X[:4,:])) print(y[:4]) print(model.coef_) print(model.intercept_) print(model.get_params()) print(model.score(X, y)) # R^2 cofficient of determination
from sklearn.datasets import make_regression import matplotlib.pyplot as plt X,y = make_regression(n_samples=100, n_features=1, n_targets=1, noise=10) plt.scatter(X,y)
from sklearn import preprocessing import numpy as np a = np.array([[10,2.7,3.6], [-100,5,5], [120,20,40]], dtype=np.float64) print(a) print(preprocessing.scale(a))
from sklearn import preprocessing import numpy as np from sklearn.model_selection import train_test_split from sklearn.datasets import make_classification from sklearn.svm import SVC import matplotlib.pyplot as plt X,y = make_classification(n_samples=300, n_features=2, n_redundant=0, n_informative=2, random_state=22, n_clusters_per_class=1, scale=100) print(X.shape) plt.scatter(X[:,0], X[:,1],c=y) X_ = preprocessing.scale(X) X_train,X_test,y_train,y_test = train_test_split(X_,y, test_size=.3) clf =SVC() clf.fit(X_train, y_train) print(clf.score(X_test, y_test)) ## no scale X_train,X_test,y_train,y_test = train_test_split(X,y, test_size=.3) clf =SVC() clf.fit(X_train, y_train) print(clf.score(X_test, y_test))
from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier X,y =load_iris(return_X_y=True) X_train,X_test,y_train,y_test = train_test_split(X,y, random_state=4) knn = KNeighborsClassifier(n_neighbors=5) knn.fit(X_train,y_train) print(knn.score(X_test, y_test))
from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier from sklearn.model_selection import cross_val_score X,y =load_iris(return_X_y=True) knn = KNeighborsClassifier(n_neighbors=5) score = cross_val_score(knn, X, y, cv=5, scoring="accuracy") print(score) print(score.mean())
from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier from sklearn.model_selection import cross_val_score import matplotlib.pyplot as plt X,y =load_iris(return_X_y=True) k_range = range(1,31) k_score = [] for k in k_range: knn = KNeighborsClassifier(n_neighbors=k) score = cross_val_score(knn, X, y, cv=5, scoring="accuracy") k_score.append(score.mean()) plt.plot(k_range, k_score) plt.xlabel("Value of K for KNN") plt.ylabel("Cross-Validated Accuracy")
图片alt
from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier from sklearn.model_selection import cross_val_score import matplotlib.pyplot as plt X,y =load_iris(return_X_y=True) k_range = range(1,31) k_score = [] for k in k_range: knn = KNeighborsClassifier(n_neighbors=k) loss = -cross_val_score(knn, X, y, cv=10, scoring="neg_mean_squared_error") k_score.append(loss.mean()) plt.plot(k_range, k_score) plt.xlabel("Value of K for KNN") plt.ylabel("Cross-Validated Accuracy")
from sklearn.model_selection import learning_curve from sklearn.datasets import load_digits from sklearn.svm import SVC import matplotlib.pyplot as plt import numpy as np X,y = load_digits(return_X_y=True) train_sizes,train_loss,test_loss = learning_curve(SVC(gamma=0.001), X, y, cv=10, scoring="neg_mean_squared_error", train_sizes=[0.1,0.25,0.5,0.75,1]) train_loss_mean = -np.mean(train_loss,axis=1) print(train_loss_mean) test_loss_mean = -np.mean(test_loss,axis=1) plt.plot(train_sizes,train_loss_mean,'o-',color="r",label="Training") plt.plot(train_sizes,test_loss_mean,"o-",color="g",label="Cross-validation") plt.legend(loc="best")
测试数据集的loss不能下降
train_sizes,train_loss,test_loss = learning_curve(SVC(gamma=0.1), X, y, cv=10, scoring="neg_mean_squared_error", train_sizes=[0.1,0.25,0.5,0.75,1])
没有Overfitting, 可以减少误差
from sklearn.model_selection import validation_curve from sklearn.datasets import load_digits from sklearn.svm import SVC import matplotlib.pyplot as plt import numpy as np X,y = load_digits(return_X_y=True) param_range = np.logspace(-6, -2.3,5) train_loss,test_loss = validation_curve(SVC(), X, y, param_name="gamma", param_range=param_range, cv=10, scoring="neg_mean_squared_error") train_loss_mean = -np.mean(train_loss,axis=1) test_loss_mean = -np.mean(test_loss,axis=1) plt.plot(param_range,train_loss_mean,'o-',color="r",label="Training") plt.plot(param_range,test_loss_mean,"o-",color="g",label="Cross-validation") plt.legend(loc="best")
from sklearn import svm from sklearn import datasets import pickle import joblib X,y = datasets.load_iris(return_X_y=True) clf = svm.SVC() clf.fit(X,y) with open("clf.pickle","wb") as f: pickle.dump(clf, f) with open("clf.pickle","rb") as f: clf2 = pickle.load(f) print(clf2.predict(X[0:1])) joblib.dump(clf,"clf.pkl") clf3 = joblib.load("clf.pkl") print(clf3.predict(X[0:1]))