From the course: Python for AI Projects: From Data Exploration to Impact

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Model fitting

Model fitting - Python Tutorial

From the course: Python for AI Projects: From Data Exploration to Impact

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Model fitting

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- [Instructor] We've prepped our data. Now, it's time to train our first machine learning models, using them to predict which tour product a user is most likely to purchase based on their attributes. We'll start by experimenting with a few proven ML algorithms. Logistic regression is a fast and interpretable baseline that often performs well on structured data. We'll also use random forest, a robust ensemble method that captures non-linear patterns with little tuning. And finally, we'll look at LightGBM, a gradient-boosting framework that's highly optimized for both speed and accuracy on tabular datasets. These models give us a mix of simplicity, power, and flexibility, perfect for comparing performance across different types of learners. Machine learning models are like complex machines, full of knobs and levers that control how they learn from data. These are called hyperparameters, and adjusting them can…

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