Impact learning is a supervised machine learning algorithm for resolving classification and linear or polynomial regression knowledge from examples. It also contributes to analyzing systems for competitive data. This algorithm is unique for being capable of learning from a competition, which is the impact of independent features. In other words, it is trained by the impacts of the features from the intrinsic rate of natural increase (RNI). The input to the Impact Learning is a training set of numerical data. In this work, we used six datasets related to regressions and classifications as the experiment of the Impact Learning, and the comparison indicates that it outperforms other standard machine learning regressions and classifications algorithms such as Random forest tree, SVM, Naive Bayes, Logistic regression, and so forth.
Impact Learning is a robust machine learning algorithm, which is being used widely to achieve more advanced results on many machine-learning related challenges. Impact learning is a supervised machine learning algorithm for resolving classification and linear or non-linear regression knowledge from examples. A perfect python module is developed using the tensor concept to use Impact learning in regression or classification problems. The dependency of this module is Tensorflow 2.0, Sklearn, Scipy, Numpy. To install in your machine learning environment just command pip install impact_learning.