翻译scikit-learn Cookbook-腾讯云开发者社区

开发者社区

文档建议反馈控制台

最新优惠活动

文章/答案/技术大牛

发布

翻译scikit-learn Cookbook

学习sklearn

专栏成员

78

文章

55614

阅读量

15

订阅数

Feature selection on L1 norms在L1范数下的特征选择

We're going to work with some ideas similar to those we saw in the recipe on Lasso Regression.In that recipe, we looked at the number of features that had zero coefficients.Now we're going to take this a step further and use the spareness associated with L1 norms to preprocess the features.

到不了的都叫做远方

2019-12-19

9170

Using k-NN for regression使用K-NN来做回归模型

Regression is covered elsewhere in the book, but we might also want to run a regression on "pockets" of the feature space. We can think that our dataset is subject to several data processes. If this is true, only training on similar data points is a good idea.

到不了的都叫做远方

2019-11-26

4540

Using sparsity to regularize models使用稀疏性来正则化模型

线性回归 scikit-learn 机器学习神经网络深度学习

The least absolute shrinkage and selection operator (LASSO) method is very similar to ridge regression and LARS. It's similar to Ridge Regression in the sense that we penalize our regression by some amount, and it's similar to LARS in that it can be used as a parameter selection, and it typically leads to a sparse vector of coefficients.

到不了的都叫做远方

2019-11-14

5340

Using ridge regression to overcome linear regression's shortfalls

In this recipe, we'll learn about ridge regression. It is different from vanilla linear regression;it introduces a regularization parameter to "shrink" the coefficients. This is useful when the dataset has collinear factors.

到不了的都叫做远方

2019-11-12

4060

Evaluating the linear regression model评估线性回归模型

matlab python numpy 线性回归

In this recipe, we'll look at how well our regression fits the underlying data. We fit a regression in the last recipe, but didn't pay much attention to how well we actually did it. The first question after we fit the model was clearly "How well does the m

到不了的都叫做远方

2019-11-11

9310

Fitting a line through data一条穿过数据的拟合直线

线性回归 api

Now, we get to do some modeling! It's best to start simple; therefore, we'll look at linear regression first. Linear regression is the first, and therefore, probably the most fundamental model—a straight line through data.

到不了的都叫做远方

2019-11-11

4830

2 Working with Linear Models 2 线性模型

线性回归机器学习神经网络深度学习人工智能

In this chapter, we will cover the following topics:在这章，将涵盖以下主题：

到不了的都叫做远方

2019-11-10

4510

没有更多了

社区活动

【纪录片】中国数据库前世今生

穿越半个世纪，探寻中国数据库50年的发展历程

Python精品学习库

代码在线跑，知识轻松学

博客搬家 | 分享价值百万资源包

自行/邀约他人一键搬运博客，速成社区影响力并领取好礼

技术创作特训营·精选知识专栏

往期视频·千货材料·成员作品最新动态