Just For My Daily Diary
Just For My Daily Diary
What is Trend? 什么是趋势？ The trend component of a time series represents a persistent, long-term change in the mean of the series. The trend is the slowest-moving part of a series, the part representing the largest time scale of importance. In a time series of product sales, an increasing trend might be the effect of […]
This notebook is an exercise in the Time Series course. You can reference the tutorial at this link. Introduction 简介 Run this cell to set everything up! 运行此单元完成所有设置！ # Setup feedback system from learntools.core import binder binder.bind(globals()) from learntools.time_series.ex1 import * # Setup notebook from pathlib import Path from learntools.time_series.style import * # plot style […]
Welcome to Time Series! 欢迎来到时间序列！ Forecasting is perhaps the most common application of machine learning in the real world. Businesses forecast product demand, governments forecast economic and population growth, meteorologists forecast the weather. The understanding of things to come is a pressing need across science, government, and industry (not to mention our personal lives!), and […]
Docker images for Elasticsearch are available from the Elastic Docker registry. A list of all published Docker images and tags is available at www.docker.elastic.co. The source code is in GitHub. Elasticsearch 的 Docker 镜像可从 Elastic Docker 镜像仓库获取。所有已发布的 Docker 镜像和标签的列表可在 www.docker.elastic.co 上找到。源代码位于 GitHub 中。 This package contains both free and subscription features. Start a 30-day trial […]
This notebook is an exercise in the Machine Learning Explainability course. You can reference the tutorial at this link. Set Up 设置 We have again provided code to do the basic loading, review and model-building. Run the cell below to set everything up: 我们再次提供了代码来执行基本的加载、审查和模型构建。运行下面的单元格来设置一切： import numpy as np import pandas as pd from sklearn.ensemble import […]
Recap 回顾 We started by learning about permutation importance and partial dependence plots for an overview of what the model has learned. 我们首先学习了排列重要性和部分依赖性图，以概述所学到的模型内容。 We then learned about SHAP values to break down the components of individual predictions. 然后，我们学习了 SHAP 值，以分解各个预测的组成部分。 Now we’ll expand on SHAP values, seeing how aggregating many SHAP values can give more […]
This notebook is an exercise in the Machine Learning Explainability course. You can reference the tutorial at this link. Set Up 设置 At this point, you have enough tools to put together compelling solutions to real-world problems. You will ned to pick the right techniques for each part of the following data science scenario. Along […]
Introduction 简介 You’ve seen (and used) techniques to extract general insights from a machine learning model. But what if you want to break down how the model works for an individual prediction? 您已经看到（并使用）了从机器学习模型中提取一般见解的技术。但是，如果您想分解模型如何针对单个预测发挥作用，该怎么办？ SHAP Values (an acronym from SHapley Additive exPlanations) break down a prediction to show the impact of each feature. Where could you […]
This notebook is an exercise in the Machine Learning Explainability course. You can reference the tutorial at this link. Set Up 设置 Today you will create partial dependence plots and practice building insights with data from the Taxi Fare Prediction competition. 今天，您将创建部分依赖关系图，并练习使用来自 出租车费预测 竞赛的数据来阐述问题。 We have again provided code to do the basic loading, review […]