GINI vs Entropy

-Gini is intended for continuous attributes, and Entropy for attributes that occur in classes (e.g. colors
-“Gini” will tend to find the largest class, and “entropy” tends to find groups of classes that make up ~50% of the data
-“Gini” to minimize misclassification
-“Entropy” for exploratory analysis
-Some studies show this doesn’t matter – these differ less than 2% of the time
-Entropy may be a little slower to compute

Data Mining Week 1

資料探勘

定義

1. Non-trivial extraction of implicit, previously unknown and potentially useful information from data
2. The process of automatically discovering useful information in large data repositories
3. They also provide capabilities to predict the outcome of a future observation

也就是能在大資料中尋找可使用的、或要應用的資料。

KDD(knowledge discovery of database) 

Preprocessing

-將Input Data整理成可用的資訊

Postprocessing

-確保只有整理合法的和有用的結果納入決策支持系統訊

Scalabilty

-用來探勘的演算法必須有高延展性

High Dimensionality

-能接納的參數可達數萬種

來看看放入的參數種類如何分吧~