From 5d129184d3bf3d4d226739e82f2c102e392985ca Mon Sep 17 00:00:00 2001 From: julie Date: Fri, 15 Sep 2017 20:47:17 -0700 Subject: [PATCH] polish English --- 01.fit_a_line/index.html | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/01.fit_a_line/index.html b/01.fit_a_line/index.html index cdb4824..10970d6 100644 --- a/01.fit_a_line/index.html +++ b/01.fit_a_line/index.html @@ -46,7 +46,7 @@ Let us begin the tutorial with a classical problem called Linear Regression \[[1 The source code for this tutorial lives on [book/fit_a_line](https://github.com/PaddlePaddle/book/tree/develop/01.fit_a_line). For instructions on getting started with PaddlePaddle, see [PaddlePaddle installation guide](https://github.com/PaddlePaddle/book/blob/develop/README.md#running-the-book). ## Problem Setup -Suppose we have a dataset of $n$ real estate properties. These real estate properties will be referred to as *homes* in this chapter for clarity. +Suppose we have a dataset of $n$ real estate properties. Each real estate property will be referred to as **homes** in this chapter for clarity. Each home is associated with $d$ attributes. The attributes describe characteristics such the number of rooms in the home, the number of schools or hospitals in the neighborhood, and the traffic condition nearby. @@ -57,7 +57,7 @@ $$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 simlilar 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 more precise the model predicts, the closer the point is to the dotted line. +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 more precise the model predicts, the closer the point is to the dotted line.


Figure 1. Predicted Value V.S. Actual Value -- GitLab