Python

This notebook is an exercise in the Feature Engineering course. You can reference the tutorial at this link. Introduction 介绍 In this exercise, you’ll apply target encoding to features in the Ames dataset. 在本练习中，您将对 Ames 数据集中的特征应用目标编码。 Run this cell to set everything up! 运行这个单元格来设置一切！ # Setup feedback system from learntools.core import binder binder.bind(globals()) from learntools.feature_engineering_new.ex6 […]

Introduction 介绍 Most of the techniques we’ve seen in this course have been for numerical features. The technique we’ll look at in this lesson, target encoding, is instead meant for categorical features. It’s a method of encoding categories as numbers, like one-hot or label encoding, with the difference that it also uses the target to […]

This notebook is an exercise in the Feature Engineering course. You can reference the tutorial at this link. Introduction 介绍 In this exercise, you’ll work through several applications of PCA to the Ames dataset. 在本练习中，您将在 Ames 数据集上完成 PCA 的多种应用。 Run this cell to set everything up! 运行这个单元格来设置一切！ # Setup feedback system from learntools.core import binder […]

Introduction 介绍 In the previous lesson we looked at our first model-based method for feature engineering: clustering. In this lesson we look at our next: principal component analysis (PCA). Just like clustering is a partitioning of the dataset based on proximity, you could think of PCA as a partitioning of the variation in the data. […]

This notebook is an exercise in the Feature Engineering course. You can reference the tutorial at this link. Introduction 介绍 In this exercise you’ll explore our first unsupervised learning technique for creating features, k-means clustering. 在本练习中，您将探索我们第一个用于创建特征的无监督学习技术：k 均值聚类。 Run this cell to set everything up! 运行这个单元格来设置一切！ # Setup feedback system from learntools.core import binder binder.bind(globals()) from […]

Introduction 介绍 This lesson and the next make use of what are known as unsupervised learning algorithms. Unsupervised algorithms don’t make use of a target; instead, their purpose is to learn some property of the data, to represent the structure of the features in a certain way. In the context of feature engineering for prediction, […]

This notebook is an exercise in the Feature Engineering course. You can reference the tutorial at this link. Introduction 介绍 In this exercise you’ll start developing the features you identified in Exercise 2 as having the most potential. As you work through this exercise, you might take a moment to look at the data documentation […]

Introduction 介绍 Once you’ve identified a set of features with some potential, it’s time to start developing them. In this lesson, you’ll learn a number of common transformations you can do entirely in Pandas. If you’re feeling rusty, we’ve got a great course on Pandas. 一旦您确定了一组具有一定潜力的特征，就可以开始开发它们了。 在本课程中，您将学习一些完全可以在 Pandas 中完成的常见转换。 如果您感到生疏，我们有一个很棒的Pandas 课程。 We’ll use four datasets […]

This notebook is an exercise in the Feature Engineering course. You can reference the tutorial at this link. Introduction 介绍 In this exercise you’ll identify an initial set of features in the Ames dataset to develop using mutual information scores and interaction plots. 在本练习中，您将确定 Ames 数据集中的一组初始特征，以使用互信息分数和交互图进行开发。 Run this cell to set everything up! 运行这个单元格来设置一切！ # […]