同理,找到LR算法在sklearn.linear_model.LogisticRegression下,所以: 算法位置填入:linear_model 算法名填入:LogisticRegression 模型名叫做:lr_model。 程序如下: 套用模板得到程序如下: # LogisticRegression分类器 fromsklearn.linear_modelimportLogisticRegression fromsklearn...
(2)导入数据划分器 sklearn的数据划分model_selection主要功能是将数据集进行训练集和测试集的划分。 from sklearn import model_selection (3)导入K近邻模型 sklearn的分类模型KNeighborsClassifier,主要功能是构建K近邻分类模型。 from sklearn import neighbors (4)导入模型度量 sklearn的metrics主要是对模型的拟合进...
from sklearn import metrics 1.accuracy_score(y_true, y_pred, normalize=True, sample_weight=None) 参数分别为y实际类别、预测类别、返回值要求(True返回正确的样本占比,false返回的是正确分类的样本数量) eg: >>> import numpy as np >>> from sklearn.metrics import accuracy_score >>> y_pred = [...
1fromsklearnimportmetrics2fromsklearn.svmimportSVC3# fit a SVM model to the data4model = SVC()5model.fit(X, y)6print(model)7# make predictions8expected = y9predicted = model.predict(X)10# summarize the fit of the model11print(metrics.classification_report(expected, predicted))12print(met...
Examples --- >>> from sklearn.metrics import mean_absolute_percentage_error >>> y_true = [3, -0.5, 2, 7] >>> y_pred = [2.5, 0.0, 2, 8] >>> mean_absolute_percentage_error(y_true, y_pred) 0.3273... >>> y_true = [[0.5, 1], [-1, 1], [7, -6]] >>> y_pred ...
pip install -U scikit-learn 二、 scikit-learn.metrics导入与调用 有两种方式导入: 方式一: from sklearn.metrics import 评价指标函数名称 例如: from sklearn.metrics import mean_squared_error from sklearn.metrics import r2_score 调用方式为:直接使用函数名调用 ...
from sklearn.metrics import accuracy_score # 加载数据集 iris = load_iris() X = iris.data y =iris.target # 因为逻辑回归是用于二分类问题,我们这里只取两个类别的数据 # 选择类别为0和1的数据 X = X[y != 2] y = y[y != 2]
fromsklearn.metricsimportaccuracy_score importmatplotlib.pyplotasplt importnumpyasnp # Load sample data X, y = load_breast_cancer(return_X_y=True) # Split data into train and test sets X_train, X_test, y_train, y_test =...
# 需要导入模块: from sklearn import metrics [as 别名]# 或者: from sklearn.metrics importcheck_scoring[as 别名]def__init__(self, trained_model, validation_df, features, target, scoring, n_jobs=None):self.trained_model = trained_model ...
# 需要导入模块: import sklearn [as 别名]# 或者: from sklearn importmetrics[as 别名]defoptimize_model(task, param_name, test_size: float, binary=False)->None:x, y = task.create_train_data()defobjective(trial):train_x, test_x, train_y, test_y = train_test_split(x, y, test_size...