“The predictive power score is an alternative to the correlation that finds more patterns in your data… detects linear and non-linear relationships. The score ranges from 0 (no predictive power) to 1 (perfect predictive power).” “The predictive power score is an asymmetric, data-type-agnostic score that can detect linear or non-linear relationships between two columns. The post ties up with this site’s summary on quantitative methods for macro information efficiency. Cursive text and text in brackets have been added for clarity. Github: ppscore – a Python implementation of the Predictive Power Score (PPS). Kaggle Predictive Power Score versus Correlation. Introducing the Predictive Power Score”, Towards Data Science, April 23 2020. For macro strategy development, predictive power score matrices can be easily created based on an existing python module and can increase the efficiency of finding hidden patterns in the data and selecting predictor variables. Technically, the score is a measurement of the success of a Decision Tree model in predicting a target variable with the help of a predictor variable out-of-sample and relative to naïve approaches. Unlike correlation, it can work with non-linear relations, categorical data, and asymmetric relations, where variable A informs on variable B more than variable B informs on variable A. Like correlation, it is suitable for quick data exploration. The predictive power score is a summary metric for predictive relations between data series.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |