diff --git a/01.fit_a_line/README.cn.md b/01.fit_a_line/README.cn.md index 3d39783faa4e5874adeda2031a47946d5c7bcf53..b028c2fde00f7d06a8fdfb4cf85b6a8a6dab7fc0 100644 --- a/01.fit_a_line/README.cn.md +++ b/01.fit_a_line/README.cn.md @@ -13,7 +13,7 @@ $$y_i = \omega_1x_{i1} + \omega_2x_{i2} + \ldots + \omega_dx_{id} + b, i=1,\ldo 初看起来，这个假设实在过于简单了，变量间的真实关系很难是线性的。但由于线性回归模型有形式简单和易于建模分析的优点，它在实际问题中得到了大量的应用。很多经典的统计学习、机器学习书籍\[[2,3,4](#参考文献)\]也选择对线性模型独立成章重点讲解。 ## 效果展示 -我们使用从[UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing)获得的波士顿房价数据集进行模型的训练和预测。下面的散点图展示了使用模型对部分房屋价格进行的预测。其中，每个点的横坐标表示同一类房屋真实价格的中位数，纵坐标表示线性回归模型根据特征预测的结果，当二者值完全相等的时候就会落在虚线上。所以模型预测得越准确，则点离虚线越近。 +我们使用从[UCI Housing Data Set](http://paddlemodels.bj.bcebos.com/uci_housing/housing.data)获得的波士顿房价数据集进行模型的训练和预测。下面的散点图展示了使用模型对部分房屋价格进行的预测。其中，每个点的横坐标表示同一类房屋真实价格的中位数，纵坐标表示线性回归模型根据特征预测的结果，当二者值完全相等的时候就会落在虚线上。所以模型预测得越准确，则点离虚线越近。


图1. 预测值 V.S. 真实值 @@ -108,7 +108,7 @@ import sys from __future__ import print_function ``` -我们通过uci_housing模块引入了数据集合[UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing) +我们通过uci_housing模块引入了数据集合[UCI Housing Data Set](http://paddlemodels.bj.bcebos.com/uci_housing/housing.data) 其中，在uci_housing模块中封装了： diff --git a/01.fit_a_line/README.md b/01.fit_a_line/README.md index 81c880867e8c601cec282ee7881521c2a3c8459b..ecb4cde694c040c0177c253a1ef513d30b2e72df 100644 --- a/01.fit_a_line/README.md +++ b/01.fit_a_line/README.md @@ -17,7 +17,7 @@ $$y_i = \omega_1x_{i,1} + \omega_2x_{i,2} + \ldots + \omega_dx_{i,d} + b, i=1,\ where $\vec{\omega}$ and $b$ are the model parameters we want to estimate. Once they are learned, we will be able to predict the price of a home, given the attributes associated with it. We call this model **Linear Regression**. In other words, we want to regress a value against several values linearly. In practice, a linear model is often too simplistic to capture the real relationships between the variables. Yet, because Linear Regression is easy to train and analyze, it has been applied to a large number of real problems. As a result, it is an important topic in many classic Statistical Learning and Machine Learning textbooks \[[2,3,4](#References)\]. ## Results Demonstration -We first show the result of our model. The dataset [UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing) is used to train a linear model to predict the home prices in Boston. The figure below shows the predictions the model makes for some home prices. The $X$-axis represents the median value of the prices of similar homes within a bin, while the $Y$-axis represents the home value our linear model predicts. The dotted line represents points where $X=Y$. When reading the diagram, the closer the point is to the dotted line, better the model's prediction. +We first show the result of our model. The dataset [UCI Housing Data Set](http://paddlemodels.bj.bcebos.com/uci_housing/housing.data) is used to train a linear model to predict the home prices in Boston. The figure below shows the predictions the model makes for some home prices. The $X$-axis represents the median value of the prices of similar homes within a bin, while the $Y$-axis represents the home value our linear model predicts. The dotted line represents points where $X=Y$. When reading the diagram, the closer the point is to the dotted line, better the model's prediction.


Figure 1. Predicted Value V.S. Actual Value @@ -127,7 +127,7 @@ except ImportError: ``` -We encapsulated the [UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing) in our Python module `uci_housing`. This module can +We encapsulated the [UCI Housing Data Set](http://paddlemodels.bj.bcebos.com/uci_housing/housing.data) in our Python module `uci_housing`. This module can 1. download the dataset to `~/.cache/paddle/dataset/uci_housing/housing.data`, if you haven't yet, and 2. [preprocess](#preprocessing) the dataset. diff --git a/01.fit_a_line/index.cn.html b/01.fit_a_line/index.cn.html index bb6869f353579d3a21b79a41e9cab196e4371305..5b234e40869cc7e63e339700dfd0b840bf3acd09 100644 --- a/01.fit_a_line/index.cn.html +++ b/01.fit_a_line/index.cn.html @@ -55,7 +55,7 @@ $$y_i = \omega_1x_{i1} + \omega_2x_{i2} + \ldots + \omega_dx_{id} + b, i=1,\ldo 初看起来，这个假设实在过于简单了，变量间的真实关系很难是线性的。但由于线性回归模型有形式简单和易于建模分析的优点，它在实际问题中得到了大量的应用。很多经典的统计学习、机器学习书籍\[[2,3,4](#参考文献)\]也选择对线性模型独立成章重点讲解。 ## 效果展示 -我们使用从[UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing)获得的波士顿房价数据集进行模型的训练和预测。下面的散点图展示了使用模型对部分房屋价格进行的预测。其中，每个点的横坐标表示同一类房屋真实价格的中位数，纵坐标表示线性回归模型根据特征预测的结果，当二者值完全相等的时候就会落在虚线上。所以模型预测得越准确，则点离虚线越近。 +我们使用从[UCI Housing Data Set](http://paddlemodels.bj.bcebos.com/uci_housing/housing.data)获得的波士顿房价数据集进行模型的训练和预测。下面的散点图展示了使用模型对部分房屋价格进行的预测。其中，每个点的横坐标表示同一类房屋真实价格的中位数，纵坐标表示线性回归模型根据特征预测的结果，当二者值完全相等的时候就会落在虚线上。所以模型预测得越准确，则点离虚线越近。


图1. 预测值 V.S. 真实值 @@ -150,7 +150,7 @@ import sys from __future__ import print_function ``` -我们通过uci_housing模块引入了数据集合[UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing) +我们通过uci_housing模块引入了数据集合[UCI Housing Data Set](http://paddlemodels.bj.bcebos.com/uci_housing/housing.data) 其中，在uci_housing模块中封装了： diff --git a/01.fit_a_line/index.html b/01.fit_a_line/index.html index 8aa4b48a5758c2b98d98f198d1925968a6f00989..8613f6c37dc171baf463f1b68b11a0652220cad6 100644 --- a/01.fit_a_line/index.html +++ b/01.fit_a_line/index.html @@ -59,7 +59,7 @@ $$y_i = \omega_1x_{i,1} + \omega_2x_{i,2} + \ldots + \omega_dx_{i,d} + b, i=1,\ where $\vec{\omega}$ and $b$ are the model parameters we want to estimate. Once they are learned, we will be able to predict the price of a home, given the attributes associated with it. We call this model **Linear Regression**. In other words, we want to regress a value against several values linearly. In practice, a linear model is often too simplistic to capture the real relationships between the variables. Yet, because Linear Regression is easy to train and analyze, it has been applied to a large number of real problems. As a result, it is an important topic in many classic Statistical Learning and Machine Learning textbooks \[[2,3,4](#References)\]. ## Results Demonstration -We first show the result of our model. The dataset [UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing) is used to train a linear model to predict the home prices in Boston. The figure below shows the predictions the model makes for some home prices. The $X$-axis represents the median value of the prices of similar homes within a bin, while the $Y$-axis represents the home value our linear model predicts. The dotted line represents points where $X=Y$. When reading the diagram, the closer the point is to the dotted line, better the model's prediction. +We first show the result of our model. The dataset [UCI Housing Data Set](http://paddlemodels.bj.bcebos.com/uci_housing/housing.data) is used to train a linear model to predict the home prices in Boston. The figure below shows the predictions the model makes for some home prices. The $X$-axis represents the median value of the prices of similar homes within a bin, while the $Y$-axis represents the home value our linear model predicts. The dotted line represents points where $X=Y$. When reading the diagram, the closer the point is to the dotted line, better the model's prediction.


Figure 1. Predicted Value V.S. Actual Value @@ -169,7 +169,7 @@ except ImportError: ``` -We encapsulated the [UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing) in our Python module `uci_housing`. This module can +We encapsulated the [UCI Housing Data Set](http://paddlemodels.bj.bcebos.com/uci_housing/housing.data) in our Python module `uci_housing`. This module can 1. download the dataset to `~/.cache/paddle/dataset/uci_housing/housing.data`, if you haven't yet, and 2. [preprocess](#preprocessing) the dataset.