Month: February 2024

In this tutorial, you will learn how to build and optimize models with gradient boosting. This method dominates many Kaggle competitions and achieves state-of-the-art results on a variety of datasets. 在本教程中，您将学习如何使用梯度提升构建和优化模型。 该方法在许多 Kaggle 竞赛中占据主导地位，并在各种数据集上取得了最好的结果。 Introduction 介绍 For much of this course, you have made predictions with the random forest method, which achieves better performance than […]

In this tutorial, you will learn how to build and optimize models with gradient boosting. This method dominates many Kaggle competitions and achieves state-of-the-art results on a variety of datasets. 在本教程中，您将学习如何使用梯度提升构建和优化模型。 该方法在许多 Kaggle 竞赛中占据主导地位，并在各种数据集上取得了最好的结果。 Introduction 介绍 For much of this course, you have made predictions with the random forest method, which achieves better performance than […]

This notebook is an exercise in the Intermediate Machine Learning course. You can reference the tutorial at this link. In this exercise, you will leverage what you’ve learned to tune a machine learning model with cross-validation. 在本练习中，您将利用所学知识通过交叉验证调整机器学习模型。 Setup 设置 The questions below will give you feedback on your work. Run the following cell to set […]

In this tutorial, you will learn how to use cross-validation for better measures of model performance. 在本教程中，您将学习如何使用交叉验证来更好地衡量模型性能。 Introduction 介绍 Machine learning is an iterative process. 机器学习是一个迭代过程。 You will face choices about what predictive variables to use, what types of models to use, what arguments to supply to those models, etc. So far, you have made […]

This notebook is an exercise in the Intermediate Machine Learning course. You can reference the tutorial at this link. In this exercise, you will use pipelines to improve the efficiency of your machine learning code. 在本练习中，您将使用管道来提高机器学习代码的效率。 Setup 设置 The questions below will give you feedback on your work. Run the following cell to set up […]

In this tutorial, you will learn how to use pipelines to clean up your modeling code. 在本教程中，您将学习如何使用管道来清理建模代码。 Introduction 介绍 Pipelines are a simple way to keep your data preprocessing and modeling code organized. Specifically, a pipeline bundles preprocessing and modeling steps so you can use the whole bundle as if it were a single step. […]

This notebook is an exercise in the Intermediate Machine Learning course. You can reference the tutorial at this link. By encoding categorical variables, you’ll obtain your best results thus far! 通过编码分类变量，您将获得迄今为止最好的结果！ Setup 设置 The questions below will give you feedback on your work. Run the following cell to set up the feedback system. 以下问题将为您提供有关您工作的反馈。 运行以下单元格来设置反馈系统。 […]

In this tutorial, you will learn what a categorical variable is, along with three approaches for handling this type of data. 在本教程中，您将了解什么是分类变量，以及处理此类数据的三种方法。 Introduction 介绍 A categorical variable takes only a limited number of values. 分类变量仅采用有限数量的值。 Consider a survey that asks how often you eat breakfast and provides four options: "Never", "Rarely", "Most days", or "Every […]

This notebook is an exercise in the Intermediate Machine Learning course. You can reference the tutorial at this link. Now it’s your turn to test your new knowledge of missing values handling. You’ll probably find it makes a big difference. 现在轮到您测试您对缺失值处理的新知识了。 您可能会发现它有很大的不同。 Setup 设置 The questions will give you feedback on your work. Run the […]

In this tutorial, you will learn three approaches to dealing with missing values. Then you’ll compare the effectiveness of these approaches on a real-world dataset. 在本教程中，您将学习三种处理缺失值的方法。 然后，您将在现实数据集上比较这些方法的有效性。 Introduction 介绍 There are many ways data can end up with missing values. For example, 数据最终可能会出现缺失值的情况有很多。 例如， A 2 bedroom house won’t include a value for the size […]