最优分段(Optimal Binning)又叫监督离散化(supervised discretizaion),使用递归划分(Recursive Partitioning)将连续变量分为分段,背后是一种基于条件推断查找较佳分组的算法。 我们首先选择对连续变量进行最优分段,在连续变量的分布不满足最优分段的要求时,再考虑对连续变量进行等距分段。最优分箱的代码如下: 代码语言:...
信用评分卡开发中一般有常用的等距分段、等深分段、最优分段。其中等距分段(Equval length intervals)是指分段的区间是一致的,比如年龄以十年作为一个分段;等深分段(Equal frequency intervals)是先确定分段数量,然后令每个分段中数据数量大致相等;最优分段(Optimal Binning)又叫监督离散化(supervised discretizaion),使...
等频分段(Equal frequency intervals):先确定分段数量,然后令每个分段中数据数量大致相等; 最优分段(Optimal Binning):又叫监督离散化(supervised discretizaion),使用递归划分(Recursive Partitioning)将连续变量分为分段,背后是一种基于条件推断查找较佳分组的算法。 我们首先选择对连续变量进行最优分段,在连续变量的分布...
其中等距分段(Equval length intervals)是指分段的区间是一致的,比如年龄以十年作为一个分段;等深分段(Equal frequency intervals)是先确定分段数量,然后令每个分段中数据数量大致相等;最优分段(Optimal Binning)又叫监督离散化(supervised discretizaion),使用递归划分(Recursive Partitioning)将连续变量分为分段,背后是一...
from sklearn.tree import DecisionTreeClassifier def optimal_binning_boundary(x: pd.Series, y: pd.Series,leaf=3, nan: float = -999.,min_per=0.1) -> list: ''' 利用决策树获得最优分箱的边界值列表 leaf: 最大叶子节点数 ''' boundary = [] # 待return的分箱边界值列表 ...
其中等距分段(Equval length intervals)是指分段的区间是一致的,比如年龄以十年作为一个分段;等深分段(Equal frequency intervals)是先确定分段数量,然后令每个分段中数据数量大致相等;最优分段(Optimal Binning)又叫监督离散化(supervised discretizaion),使用递归划分(Recursive Partitioning)将连续变量分为分段,背后是一...
其中等距分段(Equval length intervals)是指分段的区间是一致的,比如年龄以十年作为一个分段;等深分段(Equal frequency intervals)是先确定分段数量,然后令每个分段中数据数量大致相等;最优分段(Optimal Binning)又叫监督离散化(supervised discretizaion),使用递归划分(Recursive Partitioning)将连续变量分为分段,背后是一...
As it has been shown, the intuition behind the KNN algorithm is one of the most direct of all the supervised machine learning algorithms. The algorithm first calculates thedistanceof a new data point to all other training data points.
fromsklearn.datasetsimportfetch_openmlfromsklearn.model_selectionimporttrain_test_splitdataset=fetch_openml(name="qsar-biodeg",parser="auto")X=dataset.data.values.astype(float)y=dataset.target.valuesX_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.5)X_prop_train,X_cal,y_pro...
Understand the key differences between CatBoost vs. XGBoost to make informed choices in your machine learning projects. Oluseye Jeremiah 10 min code-along Getting Started with Machine Learning in Python Learn the fundamentals of supervised learning by using scikit-learn. George Boorman See More...