Summary
In this quick start guide, you built a classification model using XGBoost to predict or classify irises based on their measurements. You started by downloading and installing XGBoost and other useful Python packages. Then, you ingested the data and converted it into a pandas DataFrame. You examined the data by plotting it with Seaborn to look for potential problems. Then, you split the data into a train and test set and prepared to train a model. You used the model to make predictions and classify the flowers in the test dataset. Finally, you looked at how well the model works by checking the accuracy, precision, recall, and F1 score, and by plotting a confusion matrix.
The next chapter will go into more depth on what XGBoost is doing to build that model during training and use the model during predictions.
