From 533cb4eefa4635f74634ecd9a7e565cd8c073302 Mon Sep 17 00:00:00 2001 From: juliecbd Date: Fri, 15 Sep 2017 11:12:13 -0700 Subject: [PATCH] Update README.md Polish documents --- 01.fit_a_line/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/01.fit_a_line/README.md b/01.fit_a_line/README.md index 9c9e351..c50d75d 100644 --- a/01.fit_a_line/README.md +++ b/01.fit_a_line/README.md @@ -15,7 +15,7 @@ $$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