Machine Learning with the Minimum Nescience Principle
Project description
Machine Learning
with the Minimum Nescience Principle
nescience
is a highly efficient open source library for machine learning based on Python and built on top of scikit-learn. The library is based on the minimum nescience principle, a novel mathematical theory that measures how well we understand a problem given a representation and a description. In case of machine learning, representations are based on datasets, and descriptions are based on mathematical models.
The minimum nescience principle allow us to automate the common tasks performed by data scientists, from feature selection, model selection, or hyperparameters optimization.
nescience
can dramatically increase the productivity of the data scientist, reducing the time to analyze and model a dataset. With nescience
we can have results in very short time, without decreasing the accuracy (in fact, we usually have a better accuracy). Nescience
is fast because:
- It does not requires cross-validation
- It use a greedy search for hyperparameters
- It is not based on ensembles of models
The Library
The nescience
library is composed of the following classes:
Miscoding
measures the quality of the dataset we are using to represent our problem.Inaccuracy
measures the error made by the model we have trained.Surfeit
measures how (unnecessarily) complex is the model we have identified.
All these metrics are combined into a single quantity, called Nescience
, as a measure of how well we understand our problem given a dataset and a model. Nescience
allow us to evaluate and compare models from different model families.
The nescience
library also contains the following utilities:
Anomalies
for the identification and classification of anomalies.Causal
for cause-effect analysis.
Besides to these classes, the nescience
library provide the following automated machine-learning tools:
AutoRegression
for automated regression problems.AutoClassification
for automated classification problems.TimeSeries
for time series based analysis and forecasting.
User Guide
This user guide contains the following sections:
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.