extended-sklearn-metrics 0.1.8

A Python package for extended scikit-learn model evaluation metrics

extended-sklearn-metrics

A Python library for evaluating scikit-learn regression models with comprehensive metrics and interpretable results.

Features

• Cross-validation based model evaluation
• Automatic calculation of RMSE, MAE, R², and Explained Variance
• Error percentage calculations relative to target variable range
• Performance classification (Excellent, Good, Moderate, Poor)
• Easy-to-read summary tables

Installation

From PyPI

pip install extended-sklearn-metrics

From Source

1. Clone this repository:

git clone https://github.com/SubaashNair/extended-sklearn-metrics.git
cd extended-sklearn-metrics

2. Install dependencies:

pip install -r requirements.txt

Usage

Here's a simple example using the California Housing dataset:

from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression
from extended_sklearn_metrics import evaluate_model_with_cross_validation

# Load and prepare data
housing = fetch_california_housing(as_frame=True)
X_train, X_test, y_train, y_test = train_test_split(
    housing.data, housing.target, test_size=0.2, random_state=42
)

# Scale features
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)

# Create and evaluate model
model = LinearRegression()
target_range = y_train.max() - y_train.min()

# Get performance metrics
performance_table = evaluate_model_with_cross_validation(
    model=model,
    X=X_train_scaled,
    y=y_train,
    cv=5,
    target_range=target_range
)

print(performance_table)

Output Format

The library generates a DataFrame with the following columns:

Column	Description
Metric	Name of the metric (RMSE, MAE, R², etc.)
Value	Computed value of the metric
Threshold	Thresholds used for performance classification
Calculation	Formula/method used to compute the metric
Performance	Classification (Excellent, Good, Moderate, Poor)

Performance Thresholds

RMSE and MAE

• < 10% of range: Excellent
• 10%–20% of range: Good
• 20%–30% of range: Moderate

• 30% of range: Poor

R² and Explained Variance

• 0.7: Good

• 0.5–0.7: Acceptable
• < 0.5: Poor

Requirements

• Python 3.9+
• pandas >= 2.0.0
• scikit-learn >= 1.0.0
• numpy >= 1.20.0

License

This project is licensed under the MIT License - see the LICENSE file for details.