Data Science/Regression (5) 썸네일형 리스트형 [Models] Regression Models Note : This is just tiny subsets of full modeling workflow. We must understand domian knowledge of our training datasets and do statistical analysis firstly. import pandas as pd x_train = pd.read_csv("https://raw.githubusercontent.com/Datamanim/datarepo/main/studentscore/X_train.csv") y_train = pd.read_csv("https://raw.githubusercontent.com/Datamanim/datarepo/main/studentscore/y_train.csv") x_te.. [Models] Underfitting and Overfitting Note : This is just tiny subsets of full modeling workflow. We must understand domian knowledge of our training datasets and do statistical analysis firstly. 1. Underfitting and Overfitting Problems While we do modeling, we use reliable way to measure model accuracy. Using those metrics, we can experiment with alternative model and see which gives the best predictions. Overfitting is problem whe.. [Models] How to make model Note : This is just tiny subsets of full modeling workflow. We must understand domian knowledge of our training datasets and do statistical analysis firstly. Step 1 : Selecting data for modeling We need to start by picking a few variables using our intuition. To choose variables/columns, we need to see a list of all columns in the dataset. That is done with the column property of the DataFrame. .. [Theorem] Multivariate Linear Regression 1. Multivariate Hypothesis feet(x1) number of rooms(x2) Built Age(x3) Price of House 1412 5 30 3520 1530 3 45 2420 642 2 56 1238 \(x^{i}_{j}\) : value of feature j in ith training example \(x^i\) : the input features of the ith training example \(m\) : the number of training examples \(n\) : the number of features if \(x_{2}^{2}\), it means 45, if \(x_2\), it means [30, 45, 56] 3 dimensional vec.. [Theorem] Linear Regression 1. What is Hypothesis function? In Supervised Learning, we use 'Regression Algorithm' when we meet problem such as predicting continuous output. Using knowing data x, y in linear regression, we can predict \(y(n)\) when we have \(x(n)\) and function of \((x, y)\). Below is the function of \((x, y)\) when we have one variable. $$ H_{\theta}(x)=Y=\theta _0 + \theta _1 X $$ \(m\) : number of record.. 이전 1 다음