EMReport 0.1.0

A Python library for evaluating machine learning models with comprehensive reports for regression, classification, and clustering tasks.

EMReport

A lightweight Python utility for generating clean, readable evaluation reports for Regression, Classification, and Clustering machine learning models — all in one place.

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Features

• Regression Report — MAE, MSE, RMSE, and R²
• Classification Report — Accuracy, Precision, Recall, F1 Score, and Confusion Matrix
• Clustering Report — Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index

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Requirements

• Python 3.7+
• numpy
• scikit-learn

Install dependencies with:

pip install numpy scikit-learn

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Usage

Import the functions you need directly from EMReport.py:

from EMReport import EMReport_Regression, EMReport_Classification, EMReport_Clustering

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🔹 Regression

from EMReport import EMReport_Regression

y_true = [3.0, 5.0, 2.5, 7.0]
y_pred = [2.8, 4.9, 2.7, 6.8]

EMReport_Regression(y_true, y_pred)

Output:

--------------------------------------------------
            REGRESSION EVALUATION REPORT
--------------------------------------------------
Mean Absolute Error (MAE):       0.1500
Mean Squared Error (MSE):        0.0250
Root Mean Squared Error (RMSE):  0.1581
R-squared (R2):                  0.9921
--------------------------------------------------

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🔹 Classification

from EMReport import EMReport_Classification

y_true = [0, 1, 2, 1, 0]
y_pred = [0, 1, 1, 1, 0]

EMReport_Classification(y_true, y_pred)

Output:

--------------------------------------------------
            CLASSIFICATION EVALUATION REPORT
--------------------------------------------------
Accuracy:                        0.8000
Precision:                       0.8000
Recall:                          0.8000
F1 Score:                        0.8000
--------------------------------------------------
Confusion Matrix:
 [[2 0 0]
  [0 2 0]
  [0 1 0]]
--------------------------------------------------

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🔹 Clustering

from EMReport import EMReport_Clustering
import numpy as np

X = np.array([[1, 2], [1, 4], [1, 0],
              [10, 2], [10, 4], [10, 0]])
labels = [0, 0, 0, 1, 1, 1]

EMReport_Clustering(X, labels)

Output:

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          CLUSTERING EVALUATION REPORT
--------------------------------------------------
Number of Clusters:        2
Silhouette Score:          0.8571 (Closer to 1 is better)
Davies-Bouldin Index:      0.2041 (Lower is better)
Calinski-Harabasz Index:   90.0000 (Higher is better)
--------------------------------------------------

Note: The clustering report requires at least 2 distinct clusters and fewer clusters than total samples. An error message will be shown if this condition is not met.

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API Reference

EMReport_Regression(y_true, y_pred)

Parameter	Type	Description
y_true	array-like	Ground truth target values
y_pred	array-like	Estimated target values from the model

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EMReport_Classification(y_true, y_pred)

Parameter	Type	Description
y_true	array-like	Ground truth class labels
y_pred	array-like	Predicted class labels from the model

Uses weighted averaging for Precision, Recall, and F1 Score to handle class imbalance.

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EMReport_Clustering(X, labels)

Parameter	Type	Description
X	array-like of shape (n_samples, n_features)	Input feature matrix used for clustering
labels	array-like	Cluster labels assigned to each sample

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Metrics Explained

Metric	Task	Interpretation
MAE	Regression	Average absolute difference between predictions and actuals
MSE	Regression	Average squared difference; penalises large errors more
RMSE	Regression	Square root of MSE; same unit as target variable
R²	Regression	Proportion of variance explained (1.0 = perfect fit)
Accuracy	Classification	Fraction of correctly classified samples
Precision	Classification	Of all positive predictions, how many were correct
Recall	Classification	Of all actual positives, how many were found
F1 Score	Classification	Harmonic mean of Precision and Recall
Silhouette Score	Clustering	Measures how similar a point is to its own cluster vs. others (-1 to 1)
Davies-Bouldin Index	Clustering	Average similarity between clusters; lower = better separation
Calinski-Harabasz Index	Clustering	Ratio of between-cluster to within-cluster dispersion; higher = better

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License

This project is open-source and available under the MIT License.