本文将介绍三种常用的时间序列特征工程(Feature Engineering for Time Series)方法:使用虚拟变量(dummy variables)、周期性编码(cyclical encoding)和径向基函数(radial basis functions)。这些方法可以帮助我们更好地理解和利用时间序列数据中的模式和趋势,从而提高机器学习模型的性能和预测准确度。 我们借助 scikit-lego(ht...
The genrfeatures function enables you to automate the feature engineering process in the context of a machine learning workflow. Before passing tabular training data to a regression model, you can create new features from the predictors in the data by using genrfeatures. Use the returned data to...
本篇为继Feature Engineering.特征选择方法(上)的下篇,本篇主要介绍3种常用方法: 基于逻辑回归的嵌入式特征选择(Embeded Feature Selection based on LR) 基于决策树的嵌入式特征选择(Embeded Feature Selection based on DT) 基于Shuffle模型评估指标变化的指标重要性特征选择(Feature Selection based on feature Shuffle...
使用L1范数作为惩罚项的线性模型(Linear models)会得到稀疏解:大部分特征对应的系数为0。当你希望减少特征的维度以用于其它分类器时,可以通过feature_selection.SelectFromModel来选择不为0的系数。特别指出,常用于此目的的稀疏预测模型有linear_model.Lasso(回归), linear_model.LogisticRegression 和 svm.LinearSVC(分类...
特征工程(Feature Engineering)是机器学习领域的一项核心任务,它通过对原始数据进行处理、转换和选择,生成对模型训练有利的特征,从而提高模型的表现。特征工程的重要性体现在以下几个方面: 提升模型性能:通过合理的特征工程,可以增加模型对数据的敏感度,从而提高模型的准确率和泛化能力。 降低过拟合风险:特征工程可以帮助...
In this paper, an INterpretable Automated Feature ENgineering (INAFEN) framework was designed for logistic regression. This framework automatically transforms the nonlinear relationships between numerical features and labels into linear relationships, conducts feature cross through association rule mining , and...
Another interesting possibility is using EC methods to perform automatic feature engineering for a deterministic regression method instead of evolving a single model; this may lead to smaller solutions that can be easy to understand. In this contribution, we evaluate an approach called Kaizen ...
With Scikit-learn, we can easily do a GridSearch over the parameters of the feature engineering transformers. With Feature-engine, we need to decide before hand which transformation we want to use. In the rest of the blog, I will compare the implementation of missing data imp...
Version History Introduced in R2021a See Also gencfeatures | genrfeatures | describe | transform | fitclinear | fitrlinear | fitcensemble | fitrensemble | fitcsvm | fitrsvm Topics Automated Feature Engineering for Classification Automated Feature Engineering for Regression...
Mutual information is a lot like correlation in that it measures a relationship between two quantities. The advantage of mutual information is that it can detectanykind of relationship, while correlation only detectslinearrelationships. (简而言之,互信息可以检测任何类型的关系,而相关性只能检测线性关系。