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ML engineering pipelines

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

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Build a Mock Random Forest Classifier in Python

Create a simple random-forest-like classifier with random majority voting between trees, including fit, predict, and predict_proba methods.

random forest mock machine learning

Python

import random


class MockRandomForest:
    def __init__(self, n_trees=10, random_state=42):
        self.n_trees = n_trees
        self.random_state = random_state
        self.classes_ = None
        self._class_counts = None
        random.seed(random_state)

    def fit(self, X, y):
        self.classes_ = sorted(…

13 0 Open

ML engineering pipelines easy

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 Evaluate Accuracy, Precision, and Recall in Python

Compute accuracy, precision, and recall for a binary classification model using scikit-learn's metrics functions.

metrics classification scikit-learn

Python

from sklearn.metrics import accuracy_score, precision_score, recall_score

if __name__ == "__main__":
    y_true = [0, 1, 1, 0, 1, 0, 1, 1]
    y_pred = [0, 1, 0, 0, 1, 0, 1, 1]

    accuracy = accuracy_score(y_true, y_pred)
    precision = precision_score(y_true, y_pred)
    recall = recall_score(y_true, y_pred)

   …

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ML engineering pipelines medium

Train Logistic Regression From Scratch in Python

Trains a binary logistic regression model using gradient descent on mock data, printing learned weights and probabilities.

logistic-regression machine-learning gradient-descent

Python

import numpy as np

# Mock data: 2 features, binary classification
X = np.array([[1, 2], [2, 3], [3, 4], [4, 5], [5, 6]])
y = np.array([0, 0, 1, 1, 1])

# Add bias term (column of ones)
X_b = np.c_[np.ones((X.shape[0], 1)), X]

# Initialize parameters
theta = np.zeros(X_b.shape[1])

# Hyperparameters
learning_rate = 0…

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