metrics

本篇文章详细阐述机器学习模型评估和参数调优。将主要围绕两个问题来阐述： “知其所以然”：当你选择的一个机器学习模型运行时，你要知道它是如何工作的； “青出于蓝”：更进一步，你得知道如何让此机器学习模型工作的更优。 模型评估的方法 一般情况来说，F1评分或者R平方(R-Squared value)等数值评分可以告诉我们训练的机器学习模型的好坏。也有其它许多度量方式来评估拟合模型。 你应该猜出来，我将提出使用可视化的方法结合数值评分来更直观的评判机器学习模型。接下来的几个部分将分享一些有用的工具。 首先想声明的，单单一个评分或者一条线，是无法完全评估一个机器学习模型。偏离真实场景来评估机器学习模型（'good' or 'bad'）都是“耍流氓”。某个机器学习模型若可“驾驭”小样本数据集生成最多预测模型（即，命中更多预测数据集）。如果一个拟合模型比其它拟合过的模型形式或者你昨天的预测模型能够得到更好的结果，那即是好（'good'）。 下面是一些标准指标： confusion_matrix ， mean_squared_error ， r2_score ，这些可以用来评判分类器或者回归的好坏。表格中给出的是 Scikit-Learn 中的函数以及描述： 评估分类模型 ： 指标 描述 Scikit-learn函数 Precision 精准度 from sklearn.metrics

目录 资源指标: Metrics-Server 资源指标: Metric-Server介绍 Metric-Server部署 下载yaml文件 因为有墙, 所以提前下载image镜像, 当然也可以手动修改yaml相关文件 修改文件, 不然报错 创建 Metric-Server 自定义资源指标: Prometheus k8s-prometheus-adapter 项目 Prometheus 在k8s集群中部署Prometheus github地址 需要部署的服务清单 安装部署所有服务及插件 部署kube-state-metrics 部署Exporter及Node Exporter 告警系统 Alertmanager 部署prometheus服务 自定义指标适配器 k8s-prometheus-adapter HPA 自动弹性缩放 实验一: HPA 用命令行的方式创建一个带有资源限制的pod 下面我们让myapp 这个pod能自动水平扩展 实验二: HPA v2 规则一 规则二 添加自定义指标 http_requests_per_second 自定义规则参考文档： 资源指标: Metrics-Server 资源指标: Metric-Server介绍 从k8s v1.8之后, 引入了 Metric-API , 以前在使用heapster获取资源指标时, heapster有自己的获取路径,

I'm trying to calculate the Davies-Bouldin Index in Python. Here are the steps the code below tries to reproduce. 5 Steps : For each cluster, compute euclidean distances between each point to the centroid For each cluster, compute the average of these distances For each pair of clusters, compute the euclidean distance between their centroids Then, For each pair of clusters, make the sum of the average distances to their respective centroid (computed at step 2) and divide it by the distance separating them (computed at step 3). Finally, Compute the mean of all these divisions (= all indexes) to

How should I configure the Micronaut to get the /metrics in the Prometheus format ? Used: micronaut 1.0.0.M3 Now: micronaut: ... metrics: enabled: true export: prometheus: enabled: true and result: metrics name list {"names":["jvm.memory.max","executor.pool.size"...]} I need to get: metrics in the prometheus format( formats ) At the moment, we solved the problem as follows. Added a new endpoint. Or create a controller with a mapping on /metrics . The new endpoint added a return of scrape() . Correlated endpoint with /prometheus (new endpoint can not be mapped on /metrics ). Disconnected

While looking into the resource balancer and dynamic load metrics on Service Fabric, we ran into some questions (Running devbox SDK GA 2.0.135). In the Service Fabric Explorer (the portal and the standalone application) we can see that the balancing is ran very often, most of the time it is done almost instantly and this happens every second. While looking at the Load Metric Information on the nodes or partitions it is not updating the values as we report load. We send a dynamic load report based on our interaction (a HTTP request to a service), increasing the reported load data of a single

I have a Visual Studio 2005 Solution workspace which in turn has 8 projects included in it. I want to profile the complete code(all the projects) and get some measure about the absolute cycles taken by each function to execute, or at least percentage cycle consumptions. I checked out help for VS 2005, and also the project setiings options but could not find any pointers on hwo to get the profile info. Any help regarding this would be beneficial. -AD. If your application is not particularly processor intensive, redgate ANTS Profiler is a good choice - the line-by-line stats can come in quite

I'm trying to run a LSTM, and when I use the code below: model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy', 'f1score', 'precision', 'recall']) It returns: ValueError: ('Unknown metric function', ':f1score'). I've done my searches and found this url: https://github.com/fchollet/keras/issues/5400 The "metrics" in the "model.compile" part in this url is exactly the same as mine, and no errors are returned. I suspect you are using Keras 2.X. As explained in https://keras.io/metrics/ , you can create custom metrics. These metrics appear to take only (y_true, y_pred)

I read the section Metrics on spark website . I wish to try it on the wordcount example, I can't make it work. spark/conf/metrics.properties : # Enable CsvSink for all instances *.sink.csv.class=org.apache.spark.metrics.sink.CsvSink # Polling period for CsvSink *.sink.csv.period=1 *.sink.csv.unit=seconds # Polling directory for CsvSink *.sink.csv.directory=/home/spark/Documents/test/ # Worker instance overlap polling period worker.sink.csv.period=1 worker.sink.csv.unit=seconds # Enable jvm source for instance master, worker, driver and executor master.source.jvm.class=org.apache.spark.metrics

前言 在上一篇文章 你公司到底需不需要引入实时计算引擎？ 中我讲解了日常中常见的实时需求，然后分析了这些需求的实现方式，接着对比了实时计算和离线计算。随着这些年大数据的飞速发展，也出现了不少计算的框架（Hadoop、Storm、Spark、Flink）。在网上有人将大数据计算引擎的发展分为四个阶段。 第一代：Hadoop 承载的 MapReduce 第二代：支持 DAG（有向无环图）框架的计算引擎 Tez 和 Oozie，主要还是批处理任务 第三代：支持 Job 内部的 DAG（有向无环图），以 Spark 为代表 第四代：大数据统一计算引擎，包括流处理、批处理、AI、Machine Learning、图计算等，以 Flink 为代表 或许会有人不同意以上的分类，我觉得其实这并不重要的，重要的是体会各个框架的差异，以及更适合的场景。并进行理解，没有哪一个框架可以完美的支持所有的场景，也就不可能有任何一个框架能完全取代另一个。 本文将对 Flink 的整体架构和 Flink 的多种特性做个详细的介绍！在讲 Flink 之前的话，我们先来看看 数据集类型 和 数据运算模型 的种类。 数据集类型 无穷数据集：无穷的持续集成的数据集合 有界数据集：有限不会改变的数据集合 那么那些常见的无穷数据集有哪些呢？ 用户与客户端的实时交互数据 应用实时产生的日志 金融市场的实时交易记录 …

Of course the best metric would be a happiness of your users. But what metrics do you know for GUI usability measurements? For example, one of the common metrics is a average click count to perform action. What other metrics do you know? Jakob Nielsen has several articles regarding usability metrics, including one that is entitled, well, Usability Metrics : The most basic measures are based on the definition of usability as a quality metric: success rate (whether users can perform the task at all), the time a task requires, the error rate, and users' subjective satisfaction. I just look at