Understanding Machine Learning algorithms and the impact of hyperparameters on their performance: Random Forest
The applet has been created to allow the students to test and became more familiar with Machine Learning fundamentals and basic algorithms.In particular, you could test Random Forest (RF) on a classification task. You can choose between three different datasets (Moons, Circles, linearly separable) and set noise, number of samples and distance between classes, but most of all you can tune the main model’s hyperparameters: number of trees in the forest and number of features to consider deciding the best split.The applet has been realized using Dash and Plotly.
Scientific Area:
Learning
Language/Environments:
Python
Target Group:
Basic
Keywords:
Regression, classification, random, Kernel Methods, Support vector machine, Ensemble Methods, Error Estimation
Start the applet!