sklearn.metrics模块通常不需要单独安装,因为它已经包含在scikit-learn库中。当你安装scikit-learn时,sklearn.metrics模块会自动包含在内。 如果你还没有安装scikit-learn,你可以通过以下步骤来安装它,从而间接安装sklearn.metrics模块: 使用pip安装scikit-learn: bash pip install scikit-learn 使用conda安装scikit-lear...
print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred)) print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred)) print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred))) 五、总结 sklearn是一个功能强大且易于使用的Pyt...
模型选择:比较,验证,选择参数和模型,常用的模块有:grid search(网格搜索)、cross validation(交叉验证)、 metrics(度量)。它的目标是通过参数调整提高精度。 预处理:特征提取和归一化,常用的模块有:preprocessing,feature extraction,常见的应用有:把输入数据(如文本)转换为机器学习算法可用的数据。 降维:减少要考虑的...
from sklearn.model_selection import train_test_split from sklearn.metrics import mean_absolute_error data = load_boston()clf = LinearRegression()X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.3, random_state=0)clf.fit(X_train, y...
from sklearn.metrics import mean_absolute_error #利用线性回归模型预测波斯顿房价 #下载sklearn自带的数据集 data = load_boston() #建立线性回归模型 clf = LinearRegression() #划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.3, rando...
from sklearn.metricsimportmean_squared_error,r2_score # 导入糖尿病数据集 diabetes_X,diabetes_y=datasets.load_diabetes(return_X_y=True)# Use only one feature diabetes_X=diabetes_X[:,np.newaxis,2]# 取第三列bmi的值 # Split the data into training/testing sets ...
fromsklearn.neighborsimportKNeighborsClassifierfromsklearn.model_selectionimporttrain_test_splitfromsklearn.metricsimportaccuracy_score# 将数据集分为训练集和测试集X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42)# 创建并训练KNN分类器knn=KNeighborsClassifier()knn.fi...
from sklearn.metrics import mean_squared_error, r2_score print("Mean squared error: %.2f" % mean_squared_error(y_test, y_pred)) print('Variance score: %.2f' % r2_score(y_test, y_pred)) 1. 2. 3. 4. 5. 6. 7. 8.
功能有分类(SVM,nearest neighbors,random forest等),回归(SVR, ridge regression, Lasso等),聚类(k-Means, spectral clustering, mean-shift等),降维(PCA, feature selection, non-negative matrix factorization等),选择模型(grid search, cross validation, metrics等),预处理(preprocessing, feature extraction等)。
fromsklearn.svmimportSVCfromsklearn.model_selectionimporttrain_test_splitfromsklearn.metricsimportaccuracy_score X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42)svm=SVC()svm.fit(X_train,y_train)y_pred=svm.predict(X_test)accuracy=accuracy_score(y_test,y_...