I'm diving into machine learning and I want to start with a basic classification task using a Decision Tree classifier in Python. How can I train a Decision Tree classifier on a dataset and use it to make predictions? I have a dataset with features and corresponding labels. Let's assume it's a simple dataset with numeric features and binary labels (0 or 1). Here's what I have so far:
Could you provide a code example of how to preprocess this data, train a Decision Tree classifier, and use it for prediction? Additionally, any insights into hyperparameter tuning or evaluating the model's performance would be appreciated. Thank you!
Python:
# Sample dataset (features and labels)
features = [[1.2, 2.3], [2.4, 1.8], [3.5, 2.8], [2.1, 3.2]]
labels = [0, 1, 0, 1]Could you provide a code example of how to preprocess this data, train a Decision Tree classifier, and use it for prediction? Additionally, any insights into hyperparameter tuning or evaluating the model's performance would be appreciated. Thank you!
