In this project, we are exploring Scikit-learn’s classification capabilities and accuracy estimation techniques using a synthetic dataset provided during a Data Science London meetup. The goal is to develop a binary classifier to categorize 9,000 objects, each represented by 40 numerical features composed of decimal values.
This exercise utilizes a synthetic dataset with 40 features, representing objects from two distinct classes (labeled as 0 or 1). The training set consists of 1,000 samples, while the testing set contains 9,000 samples.
The chosen architecture for this practice is the Random Forest classifier.
Accuracy was calculated using the cross-validation method, with the training dataset divided into five folds.
Ben Hamner and Will Cukierski. Data Science London + Scikit-learn. https://kaggle.com/competitions/data-science-london-scikit-learn, 2013. Kaggle.