In order to achieve the best performance, a random decision forest needs to be “tuned”, which involves selecting the appropriate values for hyperparameters such as the maximum depth of the trees, the minimum number of samples required to split a node, and the number of features to consider when splitting a node. These hyperparameters can be optimized using methods such as grid search and random search.
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
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
