lindong28 commented on a change in pull request #28: URL: https://github.com/apache/flink-ml/pull/28#discussion_r754825324
########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/common/param/HasEpsilon.java ########## @@ -0,0 +1,43 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.flink.ml.common.param; + +import org.apache.flink.ml.param.DoubleParam; +import org.apache.flink.ml.param.Param; +import org.apache.flink.ml.param.ParamValidators; +import org.apache.flink.ml.param.WithParams; + +/** Interface for the shared epsilon param. */ +public interface HasEpsilon<T> extends WithParams<T> { + + Param<Double> EPSILON = + new DoubleParam( + "epsilon", + "Convergence tolerance for iterative algorithms. The default value is 0.1", + 0.1, Review comment: I have similar thoughts as @yunfengzhou-hub. Here are my findings that may be useful to consider here. Spark and Scikit-learn [1] uses HasTol for this purpose. Logistic Regression wiki [2] mentions tolerance instead of epsilon. I searched on Google for words that are commonly used for determining the "termination criteria". It looks like tolerance is much more popular than epsilon in the machine learning domain (e.g. [3]). [1] https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html [2] https://en.wikipedia.org/wiki/Logistic_regression [3] https://support.minitab.com/en-us/minitab/18/help-and-how-to/modeling-statistics/regression/how-to/nonlinear-regression/interpret-the-results/all-statistics-and-graphs/methods-and-starting-values/ -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: issues-unsubscr...@flink.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org
