RegressionMadeSimple

A minimalist machine learning toolkit on top of scikit-learn.

v4.0.0 - Latest Release

Quick Start

pip install regressionmadesimple

import regressionmadesimple as rms
import pandas as pd

# Load data
data = pd.DataFrame({
    "x": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
    "y": [2.1, 4.2, 6.1, 8.3, 10.2, 12.1, 14.3, 16.2, 18.1, 20.3]
})

# Recommended API
model = rms.models.Linear(data, "x", "y")

# Predictions
predictions = model.predict([[11], [12]])

# Metrics
summary = model.summary()
print(summary["r2_score"])
print(summary["rmse"])

# Save model
model.save_model("my_model.pkl")

Wrapper API (v4)

model = rms.LinearRegressionModel.fit(
    data,
    "x",
    "y",
    model=rms.models.Quadratic,
    testsize=0.2
)

Function API (v4)

from regressionmadesimple.api import fit
import numpy as np

X = np.array([[1], [2], [3], [4], [5]], dtype=float)
y = np.array([2.1, 4.0, 6.2, 7.9, 10.1], dtype=float)

model, results = fit(
    X,
    y,
    model=rms.models.Linear,
    split_ratio=[8, 2],
    random_state=42,
)

print(results["r2_score"])

Key Features