Classification Model and Prediction

H2O’s Gradient Boosting Machine

H2O’s Gradient Boosting Machine (GBM) is a supervised learning algorithm used for classification and regression tasks. It’s one of the most popular algorithms in machine learning due to its high accuracy and efficiency.

H2O’s GBM sequentially builds regression trees on all the features of the dataset in a fully distributed way—each tree is built in parallel and learns from the errors of the previous tree, gradually improving the overall prediction accuracy. In other words, it combines multiple weak models to create a strong model. In many cases, H2O’s GBMs are the most effective models to use due to their robustness and direct optimization of the cost function. However, there is a risk of overfitting, so it’s important to find an appropriate early stopping point during training to ensure optimal performance.

Here are some key features of H2O’s GBM:

• Gradient boosting: H2O’s GBM uses gradient boosting to improve the accuracy of the model. It starts by building a single decision tree and then uses gradient descent to minimize the error. It then builds another tree using the residuals from the first tree and repeats the process until the desired level of accuracy is achieved.

• Tuning parameters: H2O’s GBM offers several tuning parameters to optimize the model. Some important parameters include learning rate, tree depth, sample rate, and column sample rate. By tuning these parameters, we can improve the performance of the model.

• Distributed computing: H2O’s GBM is designed for distributed computing, which means it can scale up to large datasets and compute resources. It can be run on a single machine or a cluster of machines, making it highly scalable.

Train H2O’s GBM

Let’s work with the Lending Club loans dataset to build a powerful classification model using H2OGradientBoostingEstimator (H2O GBM). This dataset provides a wealth of information about loan applicants, including their employment status, credit score, and loan amount, as well as their loan status (e.g., fully paid, charged off).

To train our model, we need to carefully select the appropriate model parameters, such as the number of trees, the learning rate, and the maximum depth of the trees. Additionally, we need to specify the response and predictor variables by setting the x and y parameters, respectively. We’ll use the area under the receiver operating characteristic curve (AUC) metric for early stopping and performance evaluation. Once we’ve trained our model, we can use it to accurately predict the loan_status of new applicants with ease.