Overfitting

and data mining Paradigms Supervised learning Unsupervised... the data. [2] In a mathematical sense, these parameters represent the degree of a polynomial . The essence of overfitting is...

Data mining

In contrast, data mining uses machine learning and statistical models to uncover clandestine or hidden patterns in a large volume of data.[8] The related terms data dredging, data fishing...

[Data Science] Decision Tree - Overfitting

Overfitting and UnderfittingOverfitting (과적합, 과대적합)과적합은 모델이 학습 데이터에 대해 너무 잘 학습되어 기본 패턴 대신 데이터의 노이즈에 맞추기 시작할 때 발생한다.학습 데이터에 지나치게 맞추면(overfit) 이후 새로운 데이터(new, unseen data)에 대하여 일반화를 하지 못할 수 있다. Under...

Data Preprocessing in Data Mining

Data Cleaning: This involves identifying and correcting errors or inconsistencies in the data, such as missing values, outliers, and duplicates. Various techniques can be used for data cleaning, such as imputation, removal, and transformation. Data Integration: This involves combining data from multiple sources to create a unified dataset. Data integration can be challenging as it requires handling data with different formats, structures, and semantics. Techniques such as record linkage and data fusion can be used for data integration. ...

[Data Mining] 5. Regression - 회귀분석에서 변수선택법

앞에서 잠깐 언급했지만, 독립변수의 수가 많아지면 기본적으로 모델의 복잡도가 올라가면서 성능이 올라간다. 하지만 변수가 너무 많다면 오히려 그 성능은 낮아질 수 있다. ( 차원의 저주 : Cause of Dimensionality ) 이 때 성능이 낮아진다는 것은 예측성능이다. 즉, 학습데이터에서 성능은 높을지 몰라도 실제 현장 데이터를 대입했을 때 예측성능이 학습성능에 비해 현저히 낮게 나올 수 있다는 것이다. (overfitting) feature ...

KDD Process in Data Mining

Pre-requisites: Data Mining · In the context of computer science, “Data Mining” can be referred to as knowledge mining from data, knowledge extraction, data/pattern analysis, data archaeology, and data dredging. Data Mining also known as Knowledge Discovery in Databases, refers to the nontrivial extraction of implicit, previously unknown and potentially useful information from data stored in databases. The need of data mining is to extract useful information from large datasets and use it to make predictions or better decision-making. Nowa ...

Data Transformation in Data Mining

Data cleaning: Removing or correcting errors, inconsistencies, and missing values in the data. ; Data integration: Combining data from multiple sources, such as databases and spreadsheets, into a single format. ; Data normalization: Scaling the data to a common range of values, such as between 0 and 1, to facilitate comparison and analysis. ; Data reduction: Reducing the dimensionality of the data by selecting a subset of relevant features or attributes.

Data Transformation in Data Mining

Data Transformation in Data Mining - Data transformation is an essential phase in the data mining process. It entails transforming unprocessed data into an analytically useful format. Data transfor...

Data Mining Tutorial

Data Mining Tutorial - Data Mining is defined as the procedure of extracting information from huge sets of data. In other words, we can say that data mining is mining knowledge from data. The tutor...

[데이터마이닝] Issues in Decision Tree and Other Algorithms

Decision tree뿐만 아니라 여러 다른 data mining algorithm들에는 저마다의 issue들이 존재한다. 이번에는 decision tree를 중심으로 data mining algorithm을 적용할 때 발생하는 issue들에 대해서 알아보려고 한다.