rivalml 0.1.0

Lightweight ML benchmarking tool for reproducible model comparison

Rival

Lightweight ML benchmarking CLI tool. Compare multiple machine learning models on the same dataset with a single command. No deep learning, no cloud, no complexity -- just clean and reproducible model comparison.

Installation

pip install rival

For XGBoost support:

pip install rival[xgb]

Usage

rival run --dataset data.csv --target target_column --models lr,rf,xgb

Options

Flag	Default	Description
--dataset / -d	(required)	Path to CSV file
--target / -t	(required)	Target column name
--models / -m	lr,rf	Comma-separated: lr, rf, xgb
--missing	fill_mean	Missing value strategy: fill_mean or drop
--seed / -s	42	Random seed for reproducibility
--test-size	0.2	Test split ratio
--output / -o	(none)	Export results as JSON

Example Output

------------------------------------------------------------
MODEL      ACCURACY   PRECISION   RECALL     F1         LATENCY
------------------------------------------------------------
LR         0.8523     0.8461      0.8300     0.8380     4.2ms
RF         0.8912     0.8850      0.8720     0.8784     11.8ms
XGB        0.9076     0.9031      0.8900     0.8965     17.3ms
------------------------------------------------------------

Supported Models

• lr -- Logistic Regression (scikit-learn)
• rf -- Random Forest (scikit-learn)
• xgb -- XGBoost (optional, requires pip install rival[xgb])

Motivation

Compare ML models easily. No boilerplate, no notebooks, no scattered scripts. One command, clean output, deterministic results.