XGBoost is a powerful machine learning library that can be used to improve the performance of decision tree models. It is especially useful for large datasets and for datasets with a lot of features. In this essay, we will be discussing how to use the XGBoost library to apply a classifier on the yeast dataset from the Penn Machine Learning Benchmarks (PMLB) library.

The first step in applying XGBoost is to install the library by running the command pip install xgboost in the command prompt or terminal. Once the library is installed, it can be imported into your Python environment by using the command import xgboost as xgb.

Once the XGBoost library is imported, we can load the yeast dataset by using the command pmlb.fetch_data("yeast"). This will return a list of 14 datasets related to the yeast Saccharomyces cerevisiae. Each dataset in the list contains a different set of features and target variables. It’s important to choose the appropriate dataset for your task and to understand the characteristics of the data.

Next, we need to split our dataset into two parts: training and testing. The training dataset is used to train the model, and the testing dataset is used to evaluate the performance of the model. This can be done by using the command from sklearn.model_selection import train_test_split.

Once we have the training and testing datasets, we can create an instance of the XGBClassifier from the xgboost library by using the command from xgboost import XGBClassifier. We can then fit the classifier on the training dataset by using the command clf.fit(X_train, y_train), where X_train is the training dataset and y_train is the target variable.

After the classifier is trained, we can use it to predict the class of new data by using the command clf.predict(X_test), where X_test is the testing dataset. We can then compare the predicted class with the actual class to evaluate the performance of the model by using the command from sklearn.metrics import accuracy_score.

It’s important to note that when using XGBoost, we can specify the number of estimators that will be used, which is the number of decision trees that will be trained and combined. Additionally, we can also specify the maximum depth of each tree, the minimum number of samples required at a leaf node, and the number of features to consider when looking for the best split. These parameters can be adjusted to avoid overfitting and to improve the performance of the model.

Another important aspect of using XGBoost is to tune the learning rate, which controls the contribution of each tree to the final prediction. Lower learning rate means more trees are needed to model the data but the predictions will be more robust.

In conclusion, applying XGBoost Classifier on yeast dataset from the PMLB library is a powerful machine learning task that can be accomplished with a few simple steps. By understanding the characteristics of the data, creating a model, and training and evaluating the model, we can build powerful machine learning models that can accurately classify the yeast dataset. By adjusting the parameters of the model, we can choose the best XGBoost Classifier for our specific problem and dataset.