importcvxpyascpimportnumpyasnpfromsklearn.datasetsimportload_bostonfromsklearn.model_selectionimporttrain_test_splitfromsklearn.preprocessingimportStandardScaler# 加载数据boston=load_boston()X=boston.datay=boston.target# 数据标准化scaler=StandardScaler()X_scaled=scaler.fit_transform(X)# 划分训练集和测试集X...
y)DecisionTreeClassifier(compute_importances=None,criterion='gini',max_depth=None,max_features=None,max_leaf_nodes=None,min_density=None,min_samples_leaf=1,min_samples_split=2,random_state=None,splitter='best')>>>preds=dt.predict(X)>>>(y==preds).mean()1.0...
If you want to, you can refresh your NumPy knowledge and check out NumPy Tutorial: Your First Steps Into Data Science in Python.Application of train_test_split()You need to import train_test_split() and NumPy before you can use them. You can work in a Jupyter notebook or start a new...
数据集切分的目的是为了更好的进行模型性能评估,而更好的进行模型性能评估则是为了更好的进行模型挑选,Scikit-learn提供了train_test_split函数来帮助完成这一任务,train_test_split在model_selection模块下。 image-20230703142713957 image-20230703142909817 可以这样调用并使用它: from sklearn.model_selection import ...
from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score 准备数据集:首先,将数据集分为特征集(X)和目标变量(y)。特征集包含用于分类的各种特征,而目标变量包含类别...
使用train_test_split(),您需要提供要拆分的序列以及任何可选参数。它返回一个列表的NumPy的阵列,其它序列,或SciPy的稀疏矩阵如果合适的话: sklearn.model_selection.train_test_split(*arrays, **options) -> list arrays是list、NumPy 数组、pandas DataFrames或类似数组的对象的序列,这些对象包含要拆分的数据。所...
Using train_test_split() from the data science library scikit-learn, you can split your dataset into subsets that minimize the potential for bias in your evaluation and validation process. In this course, you’ll learn: Why you need to split your dataset in supervised machine learning Which ...
y = iris_data['label'].values print(y) 输出 [111111111111111111111111111111111111111111111111112222222222222222222222222222222222222222222222222233333333333333333333333333333333333333333333333333] 划分数据集 fromsklearn.model_selectionimporttrain_test_split X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=...
from sklearn.model_selection import train_test_splitfrom sklearn.preprocessing import StandardScalerfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.metrics import accuracy_score, classification_report, confusion_matrix# 1. ...
data = load_iris() x = data.data y = data.target x值如下,可以看到scikit-learn把数据集经过去除空值处理放在了array里,所以x是一个(150,4)的数组,保存了150个数据的4个特征: array([[5.1, 3.5, 1.4, 0.2], [4.9, 3. , 1.4, 0.2], [4.7, 3.2,...