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翻译scikit-learn Cookbook
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2021年战胜机器学习纸老虎-第二周 决策树(一)
决策树
机器学习
学习方法
神经网络
深度学习
大家好,很高兴来到了2021年战胜机器学习纸老虎-第二周 决策树(一)的内容时间。我一步一步从简单的例子,进入机器学习的世界。(PPT大小限制,压缩了会变形,就一页一页贴吧)
到不了的都叫做远方
2021-01-31
607
0
【技术创作101训练营】2021年战胜机器学习纸老虎-第一周 关于模型的思考
监督学习
机器学习
决策树
神经网络
深度学习
接触机器学习已经3年多,书是看了几本,网课也上了不少节,但由于没有真实应用,还停留在抄代码的阶段,一直没能进步。到了21年立flag的日子,就拿出它来,与自己约定,做一个系列,坚持每周一篇,从学习到应用。
到不了的都叫做远方
2021-01-20
473
0
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
357
0
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!
到不了的都叫做远方
2020-04-01
466
0
Automatic cross validation自动交叉验证
spring
scikit-learn
机器学习
神经网络
We've looked at the using cross validation iterators that scikit-learn comes with, but we can also use a helper function to perform cross validation for use automatically. This is similar to how other objects in scikit-learn are wrapped by helper functions, pipeline for instance.
到不了的都叫做远方
2019-12-10
634
0
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
416
0
Classifying data with support vector machines支持向量机用于分类数据
kernel
机器学习
神经网络
深度学习
Support vector machines (SVM) is one of the techniques we will use that doesn't have an easy probabilistic interpretation. The idea behind SVMs is that we find the plane that separates the group of the dataset the "best". Here, separation means that the choice of the plane maximizes the margin between the closest points on the plane. These points are called support vectors.
到不了的都叫做远方
2019-12-01
493
0
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
660
0
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
1.2K
0
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.
到不了的都叫做远方
2019-11-27
378
0
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:
到不了的都叫做远方
2019-11-27
326
0
Probabilistic clustering with Gaussian Mixture Models
scikit-learn
机器学习
http
神经网络
深度学习
用基于概率的高斯混合模型聚类 In KMeans, we assume that the variance of the clusters is equal. This leads to a subdivision of space that determines how the clusters are assigned; but, what about a situation where the variances are not equal and each cluster point has som
到不了的都叫做远方
2019-11-25
629
0
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.
到不了的都叫做远方
2019-11-24
674
0
Quantizing an image with KMeans clustering使用KMeans聚类量化图片
scikit-learn
机器学习
神经网络
深度学习
Image processing is an important topic in which clustering has some application.
到不了的都叫做远方
2019-11-23
1.1K
0
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
1.5K
0
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
534
0
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.
到不了的都叫做远方
2019-11-13
1.7K
0
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
451
0
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
559
0
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
1K
0
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