Data Integration is a data preprocessing technique that combines data from multiple sources such as databases (relational and non-relational), data cubes, files, etc., and provides users a unified view of these data. It gives a complete picture of key performance indicators (KPIs), customer journeys, market opportunities, etc. The data sources can be homogeneous or heterogeneous….
The main goal of feature selection’s embedded method is learning which features are the best in contributing to the accuracy of the machine learning model. They have built-in penalization functions to reduce overfitting: These encompass the benefits of both the wrapper and filter methods, by evaluating interactions of features but also maintaining reasonable computational cost….
It is a greedy optimization algorithm that aims to find the best-performing feature subset. It repeatedly creates models and keeps aside the best or the worst performing feature at each iteration. It constructs the next model with the left features until all the features are exhausted. It then ranks the features based on the order…
It is the reverse of Step Forward Feature Selection, and as you may have guessed this time, it starts with the entire set of features and works backward from there iteratively. It begins by building a machine learning model with all of the features, then creates new subsets by removing every feature, one at a…
Forward selection uses searching as a technique for selecting the best features. It is an iterative method in which we start with having no feature in the model. The step forward feature selection procedure begins by evaluating all feature subsets that consist of only one input variable. It selects the “best” feature and afterwards, adds…
Wrapper methods are performed by taking subsets and training learning algorithms. Based on the results of the training, we can select the best features for our model. And, as you may have guessed, these methods are computationally very expensive. The Wrapper methodology considers the selection of feature sets as a search problem, where different combinations…
Buying a new product or testing a new technique but not sure how it stacks up against the alternatives? It’s an all too familiar situation for most of us. Most of the options sound similar to each other so picking the best out of the lot is a challenge. In order to make a confident…
This is another statistical method that’s commonly used for testing relationships between categorical variables. Therefore, it’s suited for categorical variables and binary targets only, and the variables should be non-negative and typically Boolean frequencies or counts. What it does is simply compare the observed distribution between various features in the dataset and the target variable….
Besides duplicate features, a dataset can also include correlated features. “Correlation is defined as a measure of the linear relationship between two quantitative variables.” A high correlation is often a useful property—if two variables are highly correlated: We can predict one from the other. Therefore, we generally look for features that are highly correlated with…
Constant Features that show single values in all the observations in the dataset. These features provide no information that allows ML models to predict the target. Quasi-Constant Features in which a value occupies the majority of the records. Duplicated Features, which is self-explanatory—the same feature.
