Reference library

ML engineering pipelines

Feature prep, batch inference, model-serving hooks, and production ML workflow glue.

3 matches

How to Compute a Confusion Matrix in Python

Compute a multi-class confusion matrix from true and predicted labels using pure Python dictionaries and nested lists, then format it for readable output.

confusion-matrix classification ml-metrics

Python

from collections import defaultdict

def compute_confusion_matrix(y_true, y_pred, labels):
    """Compute confusion matrix using Python dicts and nested lists."""
    label_index = {label: i for i, label in enumerate(labels)}
    matrix = [[0] * len(labels) for _ in range(len(labels))]
    
    for true, pred in zip(y…

14 0 Open

ML engineering pipelines easy

How to Do Random Search for Hyperparameter Tuning in Python

A mock random search that samples hyperparameter combinations from a grid and ranks them by a dummy score, with a reproducible seed.

hyperparameter random-search ml

Python

import random

# Mock random search over a small hyperparameter grid
param_grid = {
    "learning_rate": [0.001, 0.01, 0.1],
    "batch_size": [16, 32, 64],
    "num_layers": [1, 2, 3]
}

def random_search(grid, n_iter=5, seed=42):
    """Perform random search over a hyperparameter grid."""
    random.seed(seed)
    k…

13 0 Open

ML engineering pipelines easy

How to do feature selection with VarianceThreshold in Python

This code demonstrates how to use scikit-learn's VarianceThreshold to remove low-variance features from a NumPy array, keeping only those that vary enough to be useful for modeling.

feature selection sklearn machine learning

Python

import numpy as np
from sklearn.feature_selection import VarianceThreshold

def main():
    # Mock dataset: 4 samples, 5 features
    X = np.array([
        [0.1, 0.2, 1.0, 1.0, 0.5],
        [0.2, 0.2, 0.0, 1.0, 0.4],
        [0.1, 0.2, 1.0, 1.0, 0.6],
        [0.3, 0.2, 1.0, 0.0, 0.5]
    ])

    # Select features w…

14 0 Open

Browse by section

Each section groups closely related Python snippets.