75 skills found · Page 3 of 3
BrooksIan / ChurnBabyChurnTelco Churn - Ensemble and Stacked Classifer Models
reichlab / Adaptively Weighted EnsembleCode and manuscript for adaptively weighted ensembles via stacking
saugatapaul1010 / Ensemble Learning BLOGEnsemble Learning — Bagging, Boosting, Stacking and Cascading Classifiers in Machine Learning using SKLEARN and MLEXTEND libraries.
haghish / AutoEnsembleautoEnsemble : An AutoML Algorithm for Building Homogeneous and Heterogeneous Stacked Ensemble Models by Searching for Diverse Base-Learners
sharmaroshan / Predicting Money Spent At ResortIt is From Analytics Vidhya Hackathons, Sponsored by Club Mahindra. It is based on Regression Problem, Where Accuracy matters the most, It is measured by RMSE Score. Different Techniques such as Stacking, Ensembling, Boosting and Scientific Operations such box-cox Operations to reduce skewness of the data.
priscilla100 / Ensemble IDSThe exponential increase in the number of connected "things" and the proliferation in the usage of Internet of Things (IoT) devices has raised numerous challenges in terms of security, privacy, and interoperability. IoT devices are resource constrained in terms of computational power, onboard memory, network bandwidth, and energy availability which limits the implementation of cryptographic solutions. The heterogeneous nature of IoT devices makes them avenue for an attacker to exploit threats like spoofing, routing, MITM, and DoS attacks. With the current sophistication of threats IoT devices are subjected to, an Intrusion Detection System (IDS) is the preferred solution for IoT devices. An IDS continuously monitors incoming traffic, and analyzes it to detect possible signs of cyber threats. This research proposes a novel intelligent ensemble-based IDS that will reside in the IoT gateway. The uniqueness of our approach is to use an ensemble learning technique which combines multiple machine learning techniques in order to the improve the predictive performance and detection accuracy. Ensemble learning have been studied to increase the detection rate while obtaining better generalization performance due to the combination of several machine learning model also known as base learners. Three popularly known ensemble models (i.e. boosting, stacking, and voting) are used in evaluating the performance of our proposed IDS using three machine learning techniques: Decision Tree, Naive Bayes (NB), and k-Nearest Neighbor (KNN). Lastly, the proposed approach will be evaluated on two publicly available dataset; Intrusion Detection Evaluation Dataset (CIC-IDS2017) and N-BaIoT.
epiforecasts / LopensembleModel stacking for predictive ensembles
zhengl0217 / Stacked Ensemble Regression ModelThis repository contains python code for stacked ensemble regression model.
ahmedshahriar / Customer Churn PredictionExtensive EDA of the IBM telco customer churn dataset, implemented various statistical hypotheses tests and Performed single-level Stacking Ensemble and tuned hyperparameters using Optuna.
Pranov1984 / Prediction Of Cement Compressive Strength Using Stacked Ensemble ModellingThe actual concrete compressive strength (MPa) for a given mixture under aspecific age (days) was determined from laboratory. Data is in raw form (not scaled).The data has 8 quantitative input variables, and 1 quantitative output variable, and 1030 instances (observations).Context:Concrete is the most important material in civil engineering. The concrete compressive strength is a highly nonlinear function of age and ingredients. These ingredients include cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, and fine aggregate. Summary of steps taken and performance achieved: Multiple models with different levels of complexity were attempted. The dependent and independent variables seem to have a nonlinear relationship as the performance of models improved with increasing complexity. MAPE was selected as the evaluation metric.Regularization, feature selection and hyper-parameter tuning was employed to improve the model performance. The models attempted are Linear Regression with no regularization Ridge and Lasso Gradient Boosting Random Forest XGboost Support Vector Machine Stacking - ensemble of the best estimators of the above tuned models with a meta regressor (i.e. Ridge) which gave the best result (MAPE of less than 10)
pankajr141 / EnsemblerPowerful stacking/blending ensemble implementation in python.
ML-GS / SELF GSA stacking ensemble learning for genomic prediction
dukenguyenxyz / Synth DetectivesALTA Shared Task 2023 - Stack Ensemble of Transformers
zhoulinumass / ML WPBE BackupStacked Ensemble Machine Learning for Range-Separation Parameters
AliHaiderAhmad001 / Stacked Embeddings And Ensemble Model Based Sequence Labeling For Aspect ExtractionGraduation project for a bachelor's degree in informatics engineering (Artificial Intelligence specialization).
RAGHVI27 / Deep Learning Based Chronic Kidney Disease DetectionA deep learning project that uses stacked ensemble approach to detect and predict Chronic Kidney disease
shayanmostafaei / Proteomic Aging Clock ProtAge Code for constructing the Proteomic Aging Clock (ProtAge) using Olink-based proteomics data and Stacked ensemble models in UK Biobank
SafwanAlselwi / AAO BiLSTMThe code and dataset used in the published paper: Smart grid stability prediction using Adaptive Aquila Optimizer and ensemble stacked BiLSTM
cchopin / Jedha Cybersec FullstackCe repository contient l'ensemble des projets, exercices et documentations réalisés dans le cadre de ma formation en cybersécurité full stack avec Jedha.
BhaveshBhakta / Multiple Disease PredictionMultiple Disease Prediction System using Machine Learning. Predicts Parkinson's, Heart Disease, and Diabetes via a web interface powered by Logistic Regression, SVM, KNN, and Stacking Ensemble. Designed for early diagnosis and healthcare support.
