There are 0 repository under stratified-cross-validation topic.
mcs_kfold stands for "monte carlo stratified k fold". This library attempts to achieve equal distribution of discrete/categorical variables in all folds. The greatest advantage of this method is that it can be applied to multi-dimensional targets.
This python program demonstrates image classification with stratified k-fold cross validation technique.
Program akan melakukan klasifikasi terhadap tweet berbahasa Indonesia yang dianggap ujaran kebencian (Hate Speech) atau tidak (Non Hate Speech).
This repo contains examples of binary classification with ANN and hyper-parameter tuning with grid search.
使用比赛方提供的脱敏数据,进行客户信贷流失预测。
Performed univariate and bivariate analysis to understand the features and their relationships for loan approval prediction. Achieved highest accuracy of 98% for Extreme Gradient Boosting among all tested machine learning classification models.
This is a machine learning project which implements three different types of regression techniques and formulates differences amongst them by predicting the price of a house based on Boston housing Data.
Credit Card Fraud Detection using Python and Machine Learning.
Object detection exercise for the Neural Networks for Computer Vision course. Using stratified KFold for data with multiple labels and instances, and self-implementation of mAP with multiple configurations.
This project aims to understand and implement all the cross validation techniques used in Machine Learning.
Melanoma is a deadly disease. Image analysis tools that automate the diagnosis of melanoma will improve dermatologists' diagnostic accuracy.
Human Activity Recognition (HAR) has a wide range of applications due to the widespread usage of acquisition devices such as smartphones and its ability to capture human activity data.
Read in a mnist file and using a neural network, predict the classifcation of 0-9 throughout the mninst dataset.
[Modeling] Project in 2022 - Simple Model of important factors in the incidence of heart disease and prediction model
Applying Convolutional Neural Networks (CNN) for recognizing manuscript digits.
Malicious URL detector built with deep exploration on feature engineering.
Enhancements to commonly used pyspark functions for modelling
Machine learning prediction project, 2019.
This project consists in using machine learning to analyze the factors that affect wine quality and in building a model for predicting it. The model was tested on unseen wines to evaluate its accuracy.
Churn prediction means detecting which customers are likely to leave a service or to cancel a subscription to a service.
Data sampling library
Data-Sprint-61---Meteorite-Threat-Identification-challenge using StratifiedKFolds and Manual Hyperparameter tuning of the algorithm
Deep Learning vs Tranditional ML methods for TB Drug Resistance prediction from Genomic data