#cross-validation

[haohanwang]

Neural networks in bankruptcy prediction: General framework and cross-validation analysis

http://www.sciencedirect.com/science/article/pii/S0377221798000514

[doyouknowkrwp]

AI 프로젝트 실패 70%는 과적합! 학습 99% vs 테스트 60% 격차 해결법. Bias-Variance Tradeoff 원리, 학습 곡선 분석, Dropout 30-50%, L2 정규화, Early Stopping, Data Augmentation 10배, Google AutoML/Tesla/Netflix 실전 사례까지. #BatchNormalization #BiasVarianceTradeoff #CrossValidation #DataAugmentation #Dropout #EarlyStopping #Generalization #L1 #L2 #LearningCurve #Overfitting #Regularization #Underfitting #과소적합 #과적합 #일반화 #정규화 #학습곡선 Read the full article

[botbark]

How to use K-Fold Cross-Validation for Imbalanced Dataset

How to use K-Fold Cross-Validation for Imbalanced Dataset

How to use K Fold Cross-Validation for Imbalanced Dataset is a short video to discuss cross-validation for Imbalanced Dataset. It requires the knowledge of Nested K-Fold Cross-Validation, Non-Nested K-Fold Cross-Validation, Stratified K-Fold Cross-Validation, Exhaustive Cross-Validation. K-Fold Cross-Validation for Imbalanced Dataset Happy Learning !!

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[botbark]

Cross-Validation in Machine Learning and K-fold Cross-Validation using Sklearn

Cross-Validation in Machine Learning and K-fold Cross-Validation using Sklearn

Cross-Validation in Machine Learning for example K-fold Cross-Validation is a short video to describe what is Cross-Validation in Machine Learning, why do we need to do cross-validation, and how to do it using sklearn. Cross-Validation in Machine Learning and K-fold Cross-Validation using Sklearn Happy Learning !!

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[ijtsrd]

A Novel GA-SVM Model For Vehicles And Pedestrial Classification In Videos

By Akintola Kolawole G."A Novel GA-SVM Model For Vehicles And Pedestrial Classification In Videos" 

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-4 , June 2017, 

URL: http://www.ijtsrd.com/papers/ijtsrd109.pdf  

http://www.ijtsrd.com/computer-science/artificial-intelligence/109/a-novel-ga-svm-model-for-vehicles-and-pedestrial-classification-in-videos/akintola-kolawole-g

open access journal of engineering, call for paper physics, ugc approved journals for computer science

The paper presents a novel algorithm for object classification in videos based on improved support vector machine (SVM) and genetic algorithm. One of the problems of support vector machine is selection of the appropriate parameters for the kernel. This has affected the accuracy of the SVM over the years. This research aims at optimizing the SVM Radial Basis kernel parameters using the genetic algorithm. Moving object classification is a requirement in smart visual surveillance systems as it allows the system to know the kind of object in the scene and be able to recognize the actions the object can perform. This paper presents an GA-SVM machine learning approach for real time object classification in videos. Radial distance signal features are extracted from the silhouettes of object detected in videos. The radial distance signals features are then normalized and fed into the GA-SVM model. The classification rate of 99.39% is achieved with the genetically trained SVM algorithm while 99.1% classification accuracy is achieved with the normal SVM. A comparison of this classifier with some other classifiers in terms of classification accuracy shows a better performance than other classifiers such as the normal SVM, Artificial neural network (ANN), Genetic Artificial neural network (GANN), K-nearest neighbor (K-NN) and K-Means classifiers. 

[diggdata]

k-fold Cross Validation in R

There are many ways to perform k-fold Cross Validation(CV) in R. Some packages like adabag, randomForest, etc allows you to perform this CV by setting a parameter in function call. But if you wish to perform some analysis within your CV like oversampling or dimensionality reduction then you have to write your own CV function. The following code allows you to perform k-fold CV on your dataset. Also, the following things are happening in the snippet:

Classification using Random Forest

Upsampling of minorities using ROSE package. Try SMOTE from DMwR package also.

Created a progress bar using PLYR package

https://gist.github.com/ankitksharma/6683552bbb8898894a09