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翻译scikit-learn Cookbook

学习sklearn

专栏作者

78

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52734

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15

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机器学习重新思考--何为机器学习

机器学习监督学习神经网络深度学习人工智能

在科技发展的今天，很多事情人们都不再亲力亲为，要么交给了专业人士，要么交给了机器。于是有一部分人称为了专业人士，一部分人成了专业忽悠，一部分人去捣鼓机器，然后一大部分人被解放出来，获得了休闲。

到不了的都叫做远方

2020-05-08

3350

数据结构学习-python实现01--0401

编程算法机器学习神经网络深度学习人工智能

经过近两年多的转行自学，乱七八糟的学了不少的东西，依然没有走到自己想要去的方向，继续学习，努力吧！

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2020-04-01

4400

Classifying documents with Naïve Bayes使用朴素贝叶斯分类文本

机器学习神经网络深度学习人工智能

Naïve Bayes is a really interesting model. It's somewhat similar to k-NN in the sense that it makes some assumptions that might oversimplify reality, but still perform well in many cases.

到不了的都叫做远方

2019-12-07

3860

Using many Decision Trees – random forests使用多棵决策树--随机森林

scikit-learn 机器学习神经网络深度学习人工智能

In this recipe, we'll use random forests for classification tasks. random forests are used because they're very robust to overfitting and perform well in a variety of situations.

到不了的都叫做远方

2019-11-29

6200

Tuning a Decision Tree model调试决策树模型

决策树机器学习神经网络深度学习人工智能

If we use just the basic implementation of a Decision Tree, it will probably not fit very well.Therefore, we need to tweak the parameters in order to get a good fit. This is very easy and won't require much effort.

到不了的都叫做远方

2019-11-28

1.2K0

Doing basic classifications with Decision Trees使用决策树做基本分类

决策树机器学习神经网络深度学习人工智能

In this recipe, we will perform basic classifications using Decision Trees. These are very nice models because they are easily understandable, and once trained in, scoring is very simple.

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2019-11-27

3560

4 Classifying Data with scikit-learn使用scikit-learn分类数据

决策树机器学习神经网络深度学习人工智能

This chapter will cover the following topics:本章将涵盖以下主题：

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2019-11-27

3190

Finding the closest objects in the feature space在特征空间中找到最接近的对象

scikit-learn 机器学习神经网络深度学习人工智能

Sometimes, the easiest thing to do is to just find the distance between two objects. We just need to find some distance metric, compute the pairwise distances, and compare the outcomes to what's expected.

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2019-11-24

6480

Directly applying Bayesian ridge regression直接使用贝叶斯岭回归

scikit-learn 机器学习神经网络深度学习人工智能

In the Using ridge regression to overcome linear regression's shortfalls recipe, we discussed the connections between the constraints imposed by ridge regression from an optimization standpoint. We also discussed the Bayesian interpretation of priors on the coefficients, which attract the mass of the density towards the prior, which often has a mean of 0 .

到不了的都叫做远方

2019-11-18

1.5K0

Optimizing the ridge regression parameter最优化岭回归参数

scikit-learn 机器学习神经网络深度学习人工智能

Once you start using ridge regression to make predictions or learn about relationships in the system you're modeling, you'll start thinking about the choice of alpha.For example, using OLS regression might show some relationship between two variables;however, when regularized by some alpha, the relationship is no longer significant. This can be a matter of whether a decision needs to be taken.

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2019-11-13

1.5K0

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

4380

Using stochastic gradient descent for regression使用随机梯度下降进行回归分析

scikit-learn 机器学习神经网络深度学习人工智能

In this recipe, we'll get our first taste of stochastic gradient descent. We'll use it for regression here, but for the next recipe, we'll use it for classification.

到不了的都叫做远方

2019-11-09

5400

Using Gaussian processes for regression降维之高斯过程

scikit-learn 机器学习神经网络深度学习人工智能

In this recipe, we'll use the Gaussian process for regression. In the linear models section,we saw how representing prior information on the coefficients was possible using Bayesian Ridge Regression.

到不了的都叫做远方

2019-11-07

9750

Imputing missing values through various strategies填充处理缺失值的不同方法

scikit-learn 机器学习神经网络深度学习人工智能

Data imputation is critical in practice, and thankfully there are many ways to deal with it.In this recipe, we'll look at a few of the strategies. However, be aware that there might be other approaches that fit your situation better.

到不了的都叫做远方

2019-10-30

8340

Working with categorical variables处理分类变量

scikit-learn 机器学习神经网络深度学习人工智能

Categorical variables are a problem. On one hand they provide valuable information; on the other hand, it's probably text—either the actual text or integers corresponding to the text—like an index in a lookup table.So, we clearly need to represent our text as integers for the model's sake, but we can't just use the id field or naively represent them. This is because we need to avoid a similar problem to the Creating binary features through thresholding recipe. If we treat data that is continuous, it must be interpreted as continuous.

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2019-10-29

8050

Creating binary features through thresholding通过阈值来生成二元特征

scikit-learn 机器学习神经网络深度学习人工智能

In the last recipe, we looked at transforming our data into the standard normal distribution.Now, we'll talk about another transformation, one that is quite different.

到不了的都叫做远方

2019-10-28

4190

scikit-learn Cookbook 00

scikit-learn 机器学习神经网络深度学习人工智能

This chapter discusses setting data, preparing data, and premodel dimensionality reduction.These are not the

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2019-10-25

4120

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