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# Linear Model Selection and Regularization Recall the linear model (Can also apply to GLM) Y=β0+β1X1+β2X2+...+βpXp+ϵY=\beta_0+\beta_1X_1+\beta_2X_2+...+\beta_pX_p+\epsilon Y=β0​+β1​X1​+β2​X2​+...+βp​Xp​+ϵ Then we consider even more general non-linear models the linear model has...

# Why resampling? These methods refit a model of interest by sampling from the training set, in order to obtain additional information about the fitted model Can be computationally expensive because they involve fitting the same statistical method multiple times using different subsets of the...

# Logistic Regression Write p(X)=Pr(Y=1∣X)p(X)=Pr(Y=1|X)p(X)=Pr(Y=1∣X) for short and consider using balance to predict default. Logistic regression uses the form: E(Y∣X)=p(X)=eβ0+β1X1+eβ0+β1X, Y∣X∼Bernoulli(p(X))E(Y|X)=p(X)=\frac{e^{\beta_0+\beta_1 X}}{1+e^{\beta_0+\beta_1...

# Linear Regression 線性回歸是一種簡單的監督式學習方法，假設 𝑌 對 𝑋1,𝑋2,…𝑋𝑝 的依賴是線性的。 雖然看起來過於簡單，但線性回歸在概念和實踐上都非常有用。 # Simple Linear Regression We assume a model: Y=β0+β1X+ϵY = \beta_0 + \beta_1 X + \epsilonY=β0​+β1​X+ϵ, where β0\beta_0β0​ and β1\beta_1β1​ are unknown constants, that represent the...

# Notation XXX: predictor variable (feature) We can refer to the input vector collectively as X=(X1,X2,...,Xp)X=(X_1, X_2, ..., X_p)X=(X1​,X2​,...,Xp​) Vectors are represented as a column vector X=(x11x12⋮x1p) X = \begin{pmatrix} x_{11} \\ x_{12} \\ \vdots...

# 需安裝軟體 種類 用途 相關資料 Anaconda 環境架構 4 Pandas DataFrame Rows: records (cases) Cols: features (variables) - 官網教學 - 基本語法 Seaborn Visualization Intro # Python 套件 # Python scientific computing https://scipy-lectures.org/ https://github.com/jakevdp/PythonDataScienceHandbook # Python for R and Matlab...

编译时多态主要指运算符重载与函数重载，而运行时多态主要指虚函数。 有基类 SHAPE ，派生类 CIRCLE ，声明如下变量： 12SHAPE shape1,*p1;CIRCLE circle1,*q1; 下列哪些项是 “派生类对象替换基类对象”。 p1=&circle1; q1=&shape1; shape1=circle1; circle1=shape1; ✔️ 令基类对象的指针指向派生类对象 ❌ 派生类指针指向基类的引用 ✔️ 派生类对象给基类对象赋值 ❌ 基类对象给派生类对象赋值 下列叙述正确的是...