Ginni has Published 1519 Articles

What is Feed-Forward Neural Networks?

Ginni

Ginni

Updated on 15-Feb-2022 06:33:26

9K+ Views

Feed-forward neural networks allows signals to travel one approach only, from input to output. There is no feedback (loops) such as the output of some layer does not influence that same layer. Feed-forward networks tends to be simple networks that associates inputs with outputs. It can be used in pattern ... Read More

What is the C5 Pruning Algorithm?

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Ginni

Updated on 15-Feb-2022 06:31:12

602 Views

C5 is the current version of the decision-tree algorithm that Australian researcher, J. Ross Quinlan has been developing and refining for several years. A prior version, ID3, established in 1986, was influential in the area of machine learning and its successors are used in multiple commercial data mining services.The trees ... Read More

What is the CART Pruning Algorithm?

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Ginni

Updated on 15-Feb-2022 06:28:43

1K+ Views

CART is a famous decision tree algorithm first produced by Leo Breiman, Jerome Friedman, Richard Olshen, and Charles Stone in 1984. CART represents Classification and Regression Trees. The CART algorithm improves binary trees and continues divided considering new splits can be found that improves purity.There are some simpler subtrees, each ... Read More

What are the types of regression in data mining?

Ginni

Ginni

Updated on 15-Feb-2022 06:18:55

3K+ Views

Regression defines a type of supervised machine learning approaches that can be used to forecast any continuous-valued attribute. Regression provides some business organization to explore the target variable and predictor variable associations. It is an essential tool to explore the data that can be used for monetary forecasting and time ... Read More

What is Regression?

Ginni

Ginni

Updated on 15-Feb-2022 06:12:59

679 Views

Regression defines a type of supervised machine learning approaches that can be used to forecast any continuous-valued attribute. Regression provides some business organization to explore the target variable and predictor variable associations. It is an essential tool to explore the data that can be used for monetary forecasting and time ... Read More

What is Orange Data Mining?

Ginni

Ginni

Updated on 14-Feb-2022 13:21:46

2K+ Views

Orange is a C++ core object and routines library that include a huge method of standard and non-standard machine learning and data mining algorithms. It is an open-source data visualization, data mining, and machine learning tool.In Orange, it is a scriptable setting for fast prototyping of the current algorithms and ... Read More

What are the types of Bitcoin Wallet?

Ginni

Ginni

Updated on 14-Feb-2022 13:18:39

376 Views

A Bitcoin wallet is a type of digital wallet can send and receive Bitcoins. This is comparable to a physical wallet. However, rather than saving a physical currency, the wallet saves the cryptographic data can access Bitcoin addresses and send transactions. There are various Bitcoin wallets can also be used ... Read More

What is Bitcoin data mining?

Ginni

Ginni

Updated on 14-Feb-2022 13:13:31

636 Views

Bitcoin mining defines the process of authenticating and inserting transactional data to the public ledger. The public ledge is called the blockchain because it includes a set of the block. Bitcoin is virtual money receiving some value, and its value is not static, it change according to time. There is ... Read More

What are the applications of CRISP-DM?

Ginni

Ginni

Updated on 14-Feb-2022 13:11:03

550 Views

The Cross Industry Standard Process for Data Mining (CRISP-DM) was recognized as an approach to further standardise the M&V methodology and allows more efficient estimation of energy savings. There are several applications of CRISP-DM which are as follows −Business Understanding − A biomedical manufacturing facility was selected as a case ... Read More

What are statistical approaches?

Ginni

Ginni

Updated on 14-Feb-2022 13:09:15

3K+ Views

Statistical approaches are model-based approaches such as a model is produced for the data, and objects are computed concerning how well they fit the model. Most statistical approaches to outlier detection are depends on developing a probability distribution model and considering how Iikely objects are below that model.An outlier is ... Read More

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