Explores time information to train a robust random forest
Project description
time-robust-forest
A Proof of concept model that explores timestamp information to train a random forest with better Out of Distribution generalization power.
Installation
pip install -U time-robust-forest
How to use it
There are a classifier and a regressor under time_robust_forest.models
. They follow the sklearn interface, which means you can quickly fit and use a model:
from time_robust_forest.models import TimeForestClassifier
features = ["x_1", "x_2"]
time_column = "periods"
target = "y"
model.fit(training_data[features + [time_column]], training_data[target])
predictions = model.predict_proba(test_data[features])[:, 1]
There are only a few arguments that differ from a traditional Random Forest. two arguments
- time_column: the column from the input dataframe containing the time periods the model will iterate over to find the best splits (default: "period")
- min_sample_periods: the number of examples in every period the model needs to keep while it splits.
- period_criterion: how the performance in every period is going to be aggregated. Options: {"avg": average, "max": maximum, the worst case}. (default: "avg")
Make sure you have a good choice for the time column
Don't simply use a timestamp column from the dataset, make it discrete before and guarantee there is a reasonable amount of data points in every period. Example: use year if you have 3+ years of data. Notice the choice to make it discrete becomes a modeling choice you can optimize.
License
This project is licensed under the terms of the BSD-3
license. See LICENSE for more details.
Citation
@misc{time-robust-forest,
author = {Moneda, Luis},
title = {Time Robust Forest model},
year = {2021},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/lgmoneda/time-robust-forest}}
}
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