形式: from sklearn.metrics import roc_auc_score auc_score = roc_auc_score(y_test,y_pred) 说明: y_pred即可以是类别,也可以是概率。 roc_auc_score直接根据真实值和预测值计算auc值,省略计算roc的过程。 1# -*- coding: utf-8 -*-2"""3# 作者:wanglei52054# 邮箱:wanglei5205@126.com5# 博...
from sklearn.datasets import fetch_california_housing data = fetch_california_housing() X, y = data.data, data.target feature_names = data.feature_names Then, we split the data set into 80% training and 20% test: from sklearn.model_selection import train_test_split X_train, X_test, y...
roc_curve函数。 步骤拆解 确认已安装scikit-learn库: 在使用scikit-learn库中的任何功能之前,确保你已经安装了它。你可以通过运行以下命令来安装scikit-learn:bash pip install scikit-learn 在Python脚本或Jupyter Notebook中,写下from sklearn.metrics import roc_curve语句: 在你的Python脚本或Jupyter Notebook中...
**kwargs:dict 传递给 matplotlib plot 函数的其他关键字参数。 返回: display:DetCurveDisplay 存储计算值的对象。 例子: >>> import matplotlib.pyplot as plt >>> from sklearn.datasets import make_classification >>> from sklearn.metrics import DetCurveDisplay >>> from sklearn.model_selection import...
import numpy as np import skfda from skfda.datasets import make_multimodal_samples, fetch_growth from skfda.preprocessing.registration import ElasticRegistration from skfda.preprocessing.registration.elastic import elastic_mean import numpy as np ### # In the example of pairwise alignment was shown ...
sklearn并非使用了课上以及书上讲的ID3算法，而是选择了CART，该算法生成二叉树；scikit-learn使用了一...
对于sklearn函数的错误消息“'RocCurveDisplay‘没有属性'from_predictions’”,这个错误消息表明在使用RocCurveDisplay函数时发生了问题。具体来说,该函数没有名为'from_predictions'的属性。 要解决这个问题,可以采取以下步骤: 确认sklearn的版本:首先,确保你正在使用最新版本的sklearn库。可以通过...
When I try to import skfda I got: ValueError Traceback (most recent call last) <ipython-input-1-7e246c0798e0> in <module> 4 #import matplotlib.pyplot as plt 5 ---> 6 import skfda 7 from skfda.datasets import fetch_growth 8 from skfda.exploratory.visualization import FPCAPlot ~/ana...
copy from https://github.com/scikit-learn/scikit-learn/tree/0.19.X 0.19.X (apachecn/sklearn-doc-zh#297) chenyyx committed Jan 3, 2018 Verified Verified This commit was created on GitHub.com and signed with GitHub’s verified signature. The key has expired. GPG key ID: 4AEE18...
from sklearn.metrics import recall_score recall_score(label, prediction) F1 ScoreThe f1 score is the harmonic mean(调和平均) of recall and precision, with a higher score as a better model.F 1=\frac{2}{\frac{1}{\text { precision }}+\frac{1}{\text { recall }}}=\frac{2 * \text...