Again select the feature with the minimum p-value. Now fit a model with two features by trying combinations of the earlier selected feature with all other remaining features. In forward selection, we start with a null model and then start fitting the model with each individual feature one at a time and select the feature with the minimum p-value. We will be fitting a regression model to predict Price by selecting optimal features through wrapper methods. There are three types of feature selection techniques :ĭifference between Filter, Wrapper, and Embedded Methods for Feature Selection It is a time-consuming approach, therefore, we use feature selection techniques to find out the smallest set of features more efficiently. Reducing over-fitting by selecting the right set of features.įor a dataset with d features, if we apply the hit and trial method with all possible combinations of features then total ( 2^d – 1) models need to be evaluated for a significant set of features. Reducing the complexity of a model and making it easier to interpret.īuilding a sensible model with better prediction power. Training a machine learning algorithm faster. So feature selection helps in finding the smallest set of features which results in Feature selection is very crucial and must component in machine learning and data science workflows especially while dealing with high-dimensional datasets.Īs the name suggests, it is a process of selecting the most significant and relevant features from a vast set of features in the given dataset.įor a dataset with d input features, the feature selection process results in k features such that k < d, where k is the smallest set of significant and relevant features. Hence, it gives an indispensable need to perform feature selection. Result in a dumb model with inaccurate or less reliable predictions. Increase in time complexity for a model to get trained. Increase in complexity of a model and makes it harder to interpret. Since some features may be irrelevant or less significant to the dependent variable so their unnecessary inclusion to the model leads to In order to perform any machine learning task or to get insights from such high dimensional data, feature selection becomes very important. In today’s era of Big data and IoT, we are easily loaded with rich datasets having extremely high dimensions. This article was published as a part of the Data Science Blogathon.
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