Using Fairlearn to Build Fair Machine Machine Learning Models with Python: Step-by-Step Towards More Responsible AI

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As we enter an era where intelligent systems are increasingly relied upon to make key decisions, responsible AI has become more critical than ever before. It’s not enough to simply rely on data-driven decision-making; we must also ensure that these systems are fair and just. But how can we do this when the same bias … Read more

Using Random Search to Tune the Hyperparameters of a Random Decision Forest with Python

Perfecting your machine learning model’s hyperparameters can often feel like hunting for a proverbial needle in a haystack. But with the Random Search algorithm, this intricate process of hyperparameter tuning can be efficiently automated, saving you valuable time and effort. Hyperparameters are properties intrinsic to your model, like the number of estimators in an ensemble … Read more

Customer Churn Prediction – Understanding Models with Feature Permutation Importance using Python

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Customer retention is a prime objective for service companies, and understanding the patterns that lead to customer churn can be the key to maintaining long-lasting client relationships. Businesses incur significant costs when customers discontinue their services, hence it’s vital to identify potential churn risks and take preemptive actions to retain these customers. Machine Learning models … Read more

Tuning Model Hyperparameters with Grid Search at the Example of Training a Random Forest Classifier in Python

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Are you struggling to find the best hyperparameters for your machine learning model? With Python’s Scikit-learn library, you can use grid search to fine-tune your model and improve its performance. In this article, we’ll guide you through the process of hyperparameter tuning for a classification model, using a random decision forest that predicts the survival … Read more