Model Evaluation and Refinement Made Easy
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Learn how to evaluate and refine predictive models using Python libraries like pandas and sklearn. Master the skills of training, testing, and optimizing model parameters to build accurate and effective predictive models.
At a Glance
Predictive modeling allows organizations to make informed decisions and allocate resources effectively by using data to predict future outcomes and trends. In this guided project, you will evaluate and refine a prediction model.
Python libraries such as pandas, sklearn can be used to evaluate and refine prediction models. With these libraries, you will train and test your model, learn about overfitting and underfitting, and find the best hyperparameter.
In this guided project, you will learn how to evaluate and refine predictive models by training and testing models, calculating cross-validation scores, and identifying overfitting and underfitting. You will also learn how to apply ridge regression and use grid search to optimize model parameters. By mastering these skills, you will be able to build predictive models that are accurate, reliable, and effective at making predictions on new data.
