机器学习案例学习【每周一例】之 Titanic: Machine Learning from Disaster

https://zhuanlan.zhihu.com/p/25185856 【Kaggle实例分析】Titanic Machine Learning from Disaster
http://blog.csdn.net/wiking__acm/article/details/42742961 Titanic: Machine Learning from Disaster(Kaggle 数据挖掘竞赛)
http://blog.csdn.net/han_xiaoyang/article/details/49797143 必读
https://github.com/yew1eb/DM-Competition-Getting-Started/tree/master/kaggle-titanic
https://duyiqi17.github.io/2017/01/29/my-first-kaggle/

时间: 2024-10-10 14:27:56

机器学习案例学习【每周一例】之 Titanic: Machine Learning from Disaster的相关文章

[kaggle入门] Titanic Machine Learning from Disaster

Titanic Data Science Solutions¶ https://www.kaggle.com/startupsci/titanic-data-science-solutions 数据挖掘竞赛七个步骤:¶ Question or problem definition. Acquire training and testing data. Wrangle, prepare, cleanse the data. Analyze, identify patterns, and explo

Kaggle竞赛题目之——Titanic: Machine Learning from Disaster

The sinking of the RMS Titanic is one of the most infamous shipwrecks in history.  On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy s

Titanic Machine Learning from Disaster

1. ipython demo http://nbviewer.ipython.org/github/agconti/kaggle-titanic/blob/master/Titanic.ipynb

机器学习---逻辑回归(二)(Machine Learning Logistic Regression II)

在<机器学习---逻辑回归(一)(Machine Learning Logistic Regression I)>一文中,我们讨论了如何用逻辑回归解决二分类问题以及逻辑回归算法的本质.现在来看一下多分类的情况. 现实中相对于二分类问题,我们更常遇到的是多分类问题.多分类问题如何求解呢?有两种方式.一种是方式是修改原有模型,另一种方式是将多分类问题拆分成一个个二分类问题解决. 先来看一下第一种方式:修改原有模型.即:把二分类逻辑回归模型变为多分类逻辑回归模型. (二分类逻辑回归称为binary

机器学习算法之旅A Tour of Machine Learning Algorithms

In this post we take a tour of the most popular machine learning algorithms. It is useful to tour the main algorithms in the field to get a feeling of what methods are available. There are so many algorithms available and it can feel overwhelming whe

Kaggle案例(一)Titanic: Machine Learning from Disaste

1. 案例简介 Titanic 案例是Kaggle 入门案例,链接地址https://www.kaggle.com/c/titanic .以下是摘自官网上的描述信息: 2. 分析数据 2.1 读取数据 加载训练数据 data_train = pd.read_csv("./input/train.csv") 预览数据 data_train.head() 训练集数据说明: 查看数据集信息 data_train.info() 查看有缺失值的列 ata_train.columns[data_t

Coursera机器学习-第六周-Advice for Applying Machine Learning

Evaluating a Learning Algorithm Desciding What to Try Next 先来看一个有正则的线性回归例子: 当在预测时,有很大的误差,该如何处理? 1.得到更多的训练样本 2.选取少量的特征 3.得到更多的特征项 4.加入特征多项式 5.减少正则项系数λ 6.增加正则项系数λ 很多人,在遇到预测结果并不理想的时候,会凭着感觉在上面的6个方案中选取一个进行,但是往往花费了大量时间却得不到改进. 于是引入了机器学习诊断,在后面会详细阐述, Evaluati

机器学习---文本特征提取之词袋模型(Machine Learning Text Feature Extraction Bag of Words)

假设有一段文本:"I have a cat, his name is Huzihu. Huzihu is really cute and friendly. We are good friends." 那么怎么提取这段文本的特征呢? 一个简单的方法就是使用词袋模型(bag of words model).选定文本内一定的词放入词袋,统计词袋内所有词出现的频率(忽略语法和单词出现的顺序),把词频(term frequency)用向量的形式表示出来. 词频统计可以用scikit-learn

kaggle _Titanic: Machine Learning from Disaster

A Data Science Framework: To Achieve 99% Accuracy https://www.kaggle.com/ldfreeman3/a-data-science-framework-to-achieve-99-accuracy/notebook 额,总共花了2天时间才把上面这个优秀回答运行完,前面还算看得懂,如何清理数据,和画图看联系 但是后面的数据处理,使用各种模型,不知道原理是什么,后面还得花点时间补一下,现在这里记录一下 疑问汇总: 第一问,第21行,左