Features#
This page lists all the features of YDF.
Learning algorithms#
CART
Random Forest
Gradient Boosted (Decision) Trees
Distributed Gradient Boosted (Decision) Trees
Meta-learning algorithm#
Automatic hyper-parameter optimizer
Supported of problems#
Classification (binary and multi-class)
Regression
Ranking
Uplift
Weighted
Supported input features#
Automatic feature type detection and dictionary building.
Numerical
Categorical
Boolean
Categorical-set
Missing
Inference#
VPred
QuickScorer Extended
Get leafs
Model evaluation#
Classification
Accuracy
AUC (Area under the curve) of the ROC curve
AUC of the Precision-Recall curve
ROC curve
Precision-Recall curve
Precision @ Recall
Recall @ Precision
Precision @ Volum
Recall @ False positive rate
False positive rate @ Recall
Cross-entropy loss
Regression
RMSE
MSE
Ranking
NDCG
MRR
Precision @ 1
Uplift
AUUC
Qini
Cate Calibration
Confidence interval
Bootstrapping
Closed-form (for a subset of metrics)
API#
CLI
C++
Python / TensorFlow
Go
JavaScript
Dataset format#
CSV
TF.Example proto of in TFRecord container
Model analysis#
Variable importance
Decision tree plotting
Tree structure statistics