How to visualise XgBoost model with learning curves in Python
XGBoost is a powerful and popular library for gradient boosting in Python. One of the ways to evaluate the performance of an XGBoost model is by using learning curves. Learning curves are plots that show how the model’s performance changes as the number of training examples increases. They can be used to diagnose problems such as overfitting or underfitting.
To create a learning curve in Python, you can use the library scikit-learn. The library has a function called learning_curve() that can be used to generate the learning curve for an XGBoost model. This function takes several parameters, including the model, the training data, and the target variable. It also takes a scoring parameter that allows you to specify the metric you want to use to evaluate the model.
The learning_curve() function returns the training and validation scores for each iteration of the model. You can then use these scores to create a plot of the learning curve using the matplotlib library. The x-axis of the plot represents the number of training examples, and the y-axis represents the model’s performance, measured by a metric such as accuracy or F1 score.
Another library that can be used to plot learning curves is Yellowbrick, it is a visualization library that provides several visual diagnostic tools, including learning curves. It also allows you to easily compare the performance of different models by plotting multiple learning curves on the same graph.
The learning curves show the model’s performance on the train and validation set as the number of training examples increases. A good model should have a good performance on both sets and the performance gap between the two sets should be small. If the gap is large, it can indicate overfitting or underfitting.
In conclusion, visualizing the XGBoost model with learning curves is a powerful technique to evaluate the performance of the model. It helps to diagnose problems such as overfitting or underfitting.
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