quick_anomaly_detector.models.trainXGB

class quick_anomaly_detector.models.trainXGB(num_epochs=1000, patience=5, lr=0.01, subsample=0.6, colsample_bytree=0.6, reg_alpha=1)

Train XGB model

__init__(num_epochs=1000, patience=5, lr=0.01, subsample=0.6, colsample_bytree=0.6, reg_alpha=1)

Methods

__init__([num_epochs, patience, lr, ...])
display_feature_importance()	display importance of xgb model
load_context(context)	Loads artifacts from the specified PythonModelContext that can be used by predict() when evaluating inputs.
log_model(model_uri[, experiment_id, ...])	If you need credential, make sure you have them in your environment:
predict(context[, model_input, params])	Evaluates a pyfunc-compatible input and produces a pyfunc-compatible output.
train(train_df, valid_df, features[, label])
xgb_predict(X[, features, label])