Feature Engineering and Selection for Regression Models with Python and Scikit-learn
Training a machine learning model is like baking a cake: the quality of the end result depends on the ingredients … Read more
Here you’ll find everything about decision forests, whether it’s tutorials on implementing decision forests in Python or tuning them with random search.
Decision forests belong to the class of ensemble models. The idea of a Random Forest is to use sets of decision trees instead of individual trees. Finding the right parameters of a decision forest can be challenging or time-consuming using the right optimization technique. Common methods for generating suitable forests are boosting or bagging. Moreover, in practice, a decision forest may have several hundred decision trees. Therefore, decision forests are less transparent in how they make decisions than single decision tree models. Nevertheless, decision forests are widely used because they often achieve excellent performance.
Training a machine learning model is like baking a cake: the quality of the end result depends on the ingredients … Read more
Perfecting your machine learning model’s hyperparameters can often feel like hunting for a proverbial needle in a haystack. But with … Read more
Have you ever received a spam email and wondered how your email provider was able to identify it as spam? … Read more
In this tutorial, we’ll be using machine learning to predict and map out crime in San Francisco. We’ll be working … Read more
Customer retention is a prime objective for service companies, and understanding the patterns that lead to customer churn can be … Read more
Are you struggling to find the best hyperparameters for your machine learning model? With Python’s Scikit-learn library, you can use … Read more