feature engineering kaggle

IntroductionIn the previous study of machine learning techniques, there was little focus on feature engineering (Feature Engineering), however, the algorithm flow that simply learns machine learning may still not use these algorithms, especially when applied to practical problems, often without knowing how to extract f

multiple and weights become multiple, so the influence of the previous continuous feature on the model is dispersed and weakened, thus reducing the risk of overfitting. )Li Yu once said: The model is to use discrete features or continuous features, in fact, is a "mass discrete features + simple model" with "small number of continuous features + complex model" trade-offs. It can be discretized with a linear model, or it can be studied with continuous

" Feature Engineering " is a gorgeous term that ensures that your predictors are encoded into the model in a way that makes the model as easy as possible to achieve good performance. For example, if you have a date field as a predictor, and it is very different in response to weekdays on weekends, it's easier to get good results by encoding dates in this way.However, this depends on many aspects.First, it i

algorithm you are using. It also involves making the best use of the data for your algorithm.How do you model your predictions to make the most of your data?This is the process of feature design and the practice of solving problems."In fact, the success of all machine learning algorithms depends on how you present the data. "--Mohammad The importance of feature designThe characteristics of your data direct

sparse.Feature SelectionAfter feature extraction and normalization, if you find that there are too many features that can cause the model to be untrained, or that it is easy to cause the model to cross-fit, you need to select the feature and pick a valuable feature. Feature selection is divided into the following type

I. What is characteristic engineering?There is a saying that is widely circulated in the industry: data and features determine the upper limit of machine learning, and models and algorithms only approximate this limit. What is the characteristic project in the end? As the name implies, its essence is an engineering activity designed to maximize the extraction of features from raw data for use by algorithms

information gain and feature engineering One of the key components of feature engineering (feature Engineering) is Feature selection (feature