H2O Package ROC Curve

The ROC (Receiver Operating Characteristic) Curve is a graphical representation of the performance of a binary classifier. It plots the True Positive Rate (TPR) against the False Positive Rate (FPR) for different threshold values.

The H2O package provides a convenient method for plotting the ROC curve for binary classification models. The h2o.performance() function computes the TPR and FPR values for different threshold values and plots the ROC curve.

Here is an example code snippet that demonstrates the use of the h2o.performance() function to plot the ROC curve for a binary classification model:

import h2o
from h2o.estimators.gbm import H2OGradientBoostingEstimator

# Initialize H2O
h2o.init()

# Import a dataset
data = h2o.import_file("path/to/dataset.csv")

# Split the dataset into training and testing sets
train, test = data.split_frame(ratios=[0.8])

# Specify the predictor and response variables
x = data.columns[:-1]
y = "response"

# Train the model
model = H2OGradientBoostingEstimator()
model.train(x=x, y=y, training_frame=train)

# Compute and plot the ROC curve for the model
perf = model.model_performance(test_data=test)
perf.plot()

In this code snippet, we first import the H2O package and initialize it. We then import a dataset and split it into training and testing sets. We specify the predictor and response variables, and train a H2OGradientBoostingEstimator model.

To compute the ROC curve for the model, we call the model_performance() method of the model object, passing in the testing data. This returns a performance object, which we then plot using the plot() method.

The resulting plot shows the ROC curve for the model, as well as the AUC (Area Under the Curve) value, which is a measure of the model's overall accuracy.

Overall, the H2O package provides a convenient and powerful way to plot the ROC curve for binary classification models.