How to apply Gradient Boosting Classifier to yeast dataset
Gradient Boosting Classifier is a powerful machine learning technique that can improve the performance of decision tree models by training multiple trees on different subsets of the data and then combining the predictions of all the trees to make a final prediction. In this essay, we will be discussing how to use scikit-learn library to apply Gradient Boosting Classifier on yeast dataset from the Penn Machine Learning Benchmarks (PMLB) library.
The first step in applying Gradient Boosting Classifier is to install the PMLB library by running the command
pip install pmlb in the command prompt or terminal. Once the library is installed, it can be imported into your Python environment by using the command
Once the PMLB 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 GradientBoostingClassifier from the scikit-learn library by using the command
from sklearn.ensemble import GradientBoostingClassifier. 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 Gradient Boosting Classifier, 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 Gradient Boosting Classifier 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 Gradient Boosting 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 Gradient Boosting Classifier for our specific problem and dataset.
In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in Python programming: How to compare Bagging ensembles in Python using yeast dataset.
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