ml-flow-model-prediction

# get predictions
y_pred = RFtree.predict(test_features)

# compute accuracy metric, then log
R2_score = r2_score(test_labels, y_pred)
print("R2_Score: ", R2_score)
mlflow.log_metric("r2", R2_score)

# plot and save plot graphic linked to model run
xgb.plot_importance(gbtree)
plt.savefig("importance.png", dpi = 200, bbox_inches = "tight")
mlflow.log_artifact("importance.png")

# log model
mlflow.sklearn.log_model(RFtree, "model")