overfitting-reduced

EnsLoss: Stochastic Calibrated Loss Ensembles for Preventing Overfitting in Classification

[MICCAI2019 & TMI2020] Overfitting under Class Imbalance: Anaylsis and Improvements for Medical Image Segmentation.

Dropout in Deep Learning

A machine learning project for predicting diabetes using data preprocessing, exploratory data analysis, and classification models.

This project demonstrates the use of multi-class SVM on the Adult Census Income dataset from the UCI Machine Learning Repository. T

Classification of signatures in image format as genuine or fake. Created two models - one from scratch using deep learning layers and other using pre trained model VGG16. Before training used image pre processing techniques as well.

Health Profile Analysis:Revealing Disorder Paterns,Medication Guidance and Risk Classification-ML Project

A Performance Study of Naive Bayes Classifier in Advertisement Analysis

Extreme precipitation classifier for Gilgit-Baltistan, built on the Karakoram ERA5 dataset.

This project explores the working of various Boosting algorithms and analyzes the results across different algorithms. Algorithms Used are: Random Forest, Ada Boost, Gradient Boost and XG Boost

The primary objective of this project is to design and train a deep neural network that can generalize well to new, unseen data, effectively distinguishing between rocks and metal cylinders based on the sonar chirp returns.

Reproducible genomic prediction study comparing L1/L2-regularized artificial neural networks with GBLUP-ADE under a frozen cross-validation design, using canonical simulated-data workflows, workflowr, and renv.

Intro to Machine Learning Course By Kaggle

This is the dataset used in the second chapter of Aurélien Géron's recent book 'Hands-On Machine learning with Scikit-Learn and TensorFlow'. It serves as an excellent introduction to implementing machine learning algorithms because it requires rudimentary data cleaning, has an easily understandable list of variables and sits at an optimal size b…

Regularization is a crucial technique in machine learning that helps to prevent overfitting. Overfitting occurs when a model becomes too complex and learns the training data so well that it fails to generalize to new, unseen data.

Deterministic checks for backtested trading strategies, run before you take them live — like npm audit for overfitting.

Reducing overfitting in perdiction in decision trees

This repository explores how data augmentation helps mitigate overfitting in CNNs with limited training data.

Ensemble learning with Bagging and Random Forest algorithms using the Iris dataset.