ML engineering pipelines
Feature prep, batch inference, model-serving hooks, and production ML workflow glue.
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.
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)
…Train Logistic Regression From Scratch in Python
Trains a binary logistic regression model using gradient descent on mock data, printing learned weights and probabilities.
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…Browse by section
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ML engineering pipelines — Python code examples
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