Kaggle-ML-How Models Work(1)

机器学习模型是怎么工作的

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Introduction

我们从机器学习模型如何工作以及怎么使用的概述开始。这可能会比较基础，如果你之前有过统计学建模或者机器学习经验。不用担心，不久后我们会向建造一个强有力的模型发展。

本课程将为你构建以下场景的模型：

你的堂兄已经投入几百美元用于炒房。由于你对数据科学感兴趣，他愿意跟你成为业务合作伙伴。他将提供资金，而你来提供能够预测各种各样房子的价格的模型。

你问你的堂兄他以前是怎么预测真实的房地产价格的。然后他说只是凭直觉。但是随着更多的质疑表明，他已经确定了他过去看过的房屋的价格模式，他利用这些模式对他正在考虑的新房进行预测。

机器学习也是同样的工作原理。我们将从一个决策树的模型开始。当然还有一些更高级的模型能够提供更准确的预测结果。但是决策树比较容易理解，而且它是数据科学中一些最好模型的基础模块。

为了方便理解，我们将从最简单的决策树开始。

它把房子分成了两类。所考虑的任何房屋的预测价格是同一类别房屋历史价格的平均。

我们使用数据来决定如何将房屋分成两组，然后再确定每组的预测价格。从数据中获取模式的这一步骤称为拟合或训练模型。用于拟合模型的数据称为训练数据。

关于模型是怎么训练的细节(例如怎么分割数据)非常复杂，我们把它留到后面再讲。在模型训练完后，你可以将它应用到新数据上，从而来预测其他房子的价格。

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Improving the Decision Tree

下面两个决策树哪个更像真实房地产训练数据拟合出来的结果？

左边的决策树 (Decision Tree 1)可能更有意义，因为它抓住了这样一个事实，即拥有更多卧室的房屋往往比卧室更少的房屋以更高的价格出售。这个模型的最大缺点就是它没有获取更多影响房价的因素，比如浴室的数目，房间大小，地理位置，等等。

你可以使用一棵有更多划分的树来获取更多影响因子。这些被称为更深的树。一棵决策树也会考虑每个房子的大小，比如像这样：

你可以通过在决策树中进行循迹来预测任何房屋的价格，只需始终选择与该房屋特征相对应的路径。房子的预测价格在树的末端。我们进行预测的末端节点称之为叶子节点(leaf)。

怎么分割以及叶子节点上的值由数据决定，所以是时候查看你将要使用的数据了。

Continue

检查你的数据.

数据下载

• sample_submission.csv
• test.csv
• train.csv

数据说明

• train.csv - the training set
• test.csv - the test set
• sample_submission.csv - a benchmark submission from a linear regression on year and month of sale, lot square footage, and number of bedrooms
• SalePrice - the property’s sale price in dollars. This is the target variable that you’re trying to predict.
• MSSubClass: The building class
• MSZoning: The general zoning classification
• LotFrontage: Linear feet of street connected to property
• LotArea: Lot size in square feet
• Street: Type of road access
• Alley: Type of alley access
• LotShape: General shape of property
• LandContour: Flatness of the property
• Utilities: Type of utilities available
• LotConfig: Lot configuration
• LandSlope: Slope of property
• Neighborhood: Physical locations within Ames city limits
• Condition1: Proximity to main road or railroad
• Condition2: Proximity to main road or railroad (if a second is present)
• BldgType: Type of dwelling
• HouseStyle: Style of dwelling
• OverallQual: Overall material and finish quality
• OverallCond: Overall condition rating
• YearBuilt: Original construction date
• YearRemodAdd: Remodel date
• RoofStyle: Type of roof
• RoofMatl: Roof material
• Exterior1st: Exterior covering on house
• Exterior2nd: Exterior covering on house (if more than one material)
• MasVnrType: Masonry veneer type
• MasVnrArea: Masonry veneer area in square feet
• ExterQual: Exterior material quality
• ExterCond: Present condition of the material on the exterior
• Foundation: Type of foundation
• BsmtQual: Height of the basement
• BsmtCond: General condition of the basement
• BsmtExposure: Walkout or garden level basement walls
• BsmtFinType1: Quality of basement finished area
• BsmtFinSF1: Type 1 finished square feet
• BsmtFinType2: Quality of second finished area (if present)
• BsmtFinSF2: Type 2 finished square feet
• BsmtUnfSF: Unfinished square feet of basement area
• TotalBsmtSF: Total square feet of basement area
• Heating: Type of heating
• HeatingQC: Heating quality and condition
• CentralAir: Central air conditioning
• Electrical: Electrical system
• 1stFlrSF: First Floor square feet
• 2ndFlrSF: Second floor square feet
• LowQualFinSF: Low quality finished square feet (all floors)
• GrLivArea: Above grade (ground) living area square feet
• BsmtFullBath: Basement full bathrooms
• BsmtHalfBath: Basement half bathrooms
• FullBath: Full bathrooms above grade
• HalfBath: Half baths above grade
• Bedroom: Number of bedrooms above basement level
• Kitchen: Number of kitchens
• KitchenQual: Kitchen quality
• TotRmsAbvGrd: Total rooms above grade (does not include bathrooms)
• Functional: Home functionality rating
• Fireplaces: Number of fireplaces
• FireplaceQu: Fireplace quality
• GarageType: Garage location
• GarageYrBlt: Year garage was built
• GarageFinish: Interior finish of the garage
• GarageCars: Size of garage in car capacity
• GarageArea: Size of garage in square feet
• GarageQual: Garage quality
• GarageCond: Garage condition
• PavedDrive: Paved driveway
• WoodDeckSF: Wood deck area in square feet
• OpenPorchSF: Open porch area in square feet
• EnclosedPorch: Enclosed porch area in square feet
• 3SsnPorch: Three season porch area in square feet
• ScreenPorch: Screen porch area in square feet
• PoolArea: Pool area in square feet
• PoolQC: Pool quality
• Fence: Fence quality
• MiscFeature: Miscellaneous feature not covered in other categories
• MiscVal: $Value of miscellaneous feature
• MoSold: Month Sold
• YrSold: Year Sold
• SaleType: Type of sale
• SaleCondition: Condition of sale

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原课程链接
Machine Learning Course Home Page.