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ML engineering pipelines

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

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How to Train a Gradient Boosting Regressor in Python

Build and evaluate a scikit-learn GradientBoostingRegressor on a synthetic dataset, printing test MSE and feature importances.

sklearn gradient-boosting regression
Python 
import numpy as np
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.metrics import mean_squared_error

def train_gradient_boosting_mock():
    # Toy regression dataset
    np.random.seed(42)
    X = np.random.rand(100, 3) * 10
    y = 2 * X[:, 0] - 1.5 * X[:, 1] + 0.5 * X[:, 2] + np.random.normal(0,…
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