193 skills found · Page 4 of 7
zengqunzhao / AIM Fair[CVPR'25] AIM-Fair: Advancing Algorithmic Fairness via Selectively Fine-Tuning Biased Models with Contextual Synthetic Data
jiangjingxue / Trajectory Tracking MPCLinear Model Predictive Controller for Vehicle Trajectory Tracking based on Kinematic Unicycle Vehicle Motion Model with Cubic Polynomial Trajectory Generation. Evolutionary Algorithm-assisted Tuning of MPC prediction horizon and penalty matrices
202201216 / PID Tuning With RLThis project uses reinforcement learning (RL) to tune PID controllers for a nonlinear mass-spring-damper system. It applies the TD3 algorithm to compare the performance of a traditional PID tuner with an RL-trained PID tuner, evaluated through simulations with step and constant inputs.
mjain72 / Hyperparameter Tuning In XGBoost Using Genetic AlgorithmNo description available
mohakraaj / IntrusionDetectionSystemA HYBRID APPROACH TO ANOMALY DETECTION USING FUZZY LOGIC TUNED WITH EVOLUTIONARY ALGORITHMS
NhanPhamThanh-IT / Random Forest Wine Quality Prediction🍾 A comprehensive machine learning project using Random Forest algorithm to predict wine quality based on physicochemical properties. Features EDA, model training, hyperparameter tuning, feature importance analysis, and detailed documentation.
bluedistro / Graph TraversalA fine-tuned visual implementation of Informed and Uninformed Search Algorithms such as Breadth First Search, Depth First Search, Uniform Cost Search, A* Search, Greedy First Search
fsrt16 / Introduction To Genomic Data Sciences Breast Cancer Detection# Breast-cancer-risk-prediction > Necessity, who is the mother of invention. – Plato* ## Welcome to my GitHub repository on Using Predictive Analytics model to diagnose breast cancer. --- ### Objective: The repository is a learning exercise to: * Apply the fundamental concepts of machine learning from an available dataset * Evaluate and interpret my results and justify my interpretation based on observed data set * Create notebooks that serve as computational records and document my thought process. The analysis is divided into four sections, saved in juypter notebooks in this repository 1. Identifying the problem and Data Sources 2. Exploratory Data Analysis 3. Pre-Processing the Data 4. Build model to predict whether breast cell tissue is malignant or Benign ### [Notebook 1](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB1_IdentifyProblem%2BDataClean.ipynb): Identifying the problem and Getting data. **Notebook goal:Identify the types of information contained in our data set** In this notebook I used Python modules to import external data sets for the purpose of getting to know/familiarize myself with the data to get a good grasp of the data and think about how to handle the data in different ways. ### [Notebook 2](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB2_ExploratoryDataAnalysis.ipynb) Exploratory Data Analysis **Notebook goal: Explore the variables to assess how they relate to the response variable** In this notebook, I am getting familiar with the data using data exploration and visualization techniques using python libraries (Pandas, matplotlib, seaborn. Familiarity with the data is important which will provide useful knowledge for data pre-processing) ### [Notebook 3](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB3_DataPreprocesing.ipynb) Pre-Processing the data **Notebook goal:Find the most predictive features of the data and filter it so it will enhance the predictive power of the analytics model.** In this notebook I use feature selection to reduce high-dimension data, feature extraction and transformation for dimensionality reduction. This is essential in preparing the data before predictive models are developed. ### [Notebook 4](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB4_PredictiveModelUsingSVM.ipynb) Predictive model using Support Vector Machine (svm) **Notebook goal: Construct predictive models to predict the diagnosis of a breast tumor.** In this notebook, I construct a predictive model using SVM machine learning algorithm to predict the diagnosis of a breast tumor. The diagnosis of a breast tumor is a binary variable (benign or malignant). I also evaluate the model using confusion matrix the receiver operating curves (ROC), which are essential in assessing and interpreting the fitted model. ### [Notebook 5](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB_5%20OptimizingSVMClassifier.ipynb): Optimizing the Support Vector Classifier **Notebook goal: Construct predictive models to predict the diagnosis of a breast tumor.** In this notebook, I aim to tune parameters of the SVM Classification model using scikit-learn.
Hmzbo / Fine Tune LLMS With GrpoFine-tune LLMs with GRPO algorithm tutorial
knightspore / SelectaTune Your Own Spotify Recommendation Algorithm
JackMansfield2019 / BrokerBotBroker Bot is an autonomous trading algorithm designed to continuously analyze New York Stock Exchange (NYSE) market conditions and execute profitable trades by utilizing advanced trading strategies. Built upon the Alpaca API, Broker Bot will be tuned through the extensive backtesting and paper trading capabilities provided.
go-playground / Backoff:bowtie: Backoff uses an exponential backoff algorithm to backoff between retries with optional auto-tuning functionality.
CzakoZoltan08 / AutoAIPSO-SA - a hybrid Particle Swarm Optimization and Simulated Annealing algorithm for automatic AI algorithm selection and tuning (for sklearn)
bitanath / Mlx Stable DiffusionA simplified mlx implementation of the original stable diffusion 1.5 algorithm featuring fine-tuned weight loading and lora loading for Text and Image generation
newking9088 / MITx 6.86x Machine Learning With Python From Linear Models To Deep Learning Fall 2020Welcome to 6.86x Machine Learning with Python–From Linear Models to Deep Learning. Machine learning methods are commonly used across engineering and sciences, from computer systems to physics. Moreover, commercial sites such as search engines, recommender systems (e.g., Netflix, Amazon), advertisers, and financial institutions employ machine learning algorithms for content recommendation, predicting customer behavior, compliance, or risk. As a discipline, machine learning tries to design and understand computer programs that learn from experience for the purpose of prediction or control. In this course, you will learn about principles and algorithms for turning training data into effective automated predictions. We will cover: Representation, over-fitting, regularization, generalization, VC dimension; Clustering, classification, recommender problems, probabilistic modeling, reinforcement learning; On-line algorithms, support vector machines, and neural networks/deep learning. You will be able to: Understand principles behind machine learning problems such as classification, regression, clustering, and reinforcement learning Implement and analyze models such as linear models, kernel machines, neural networks, and graphical models Choose suitable models for different applications Implement and organize machine learning projects, from training, validation, parameter tuning, to feature engineering You will implement and experiment with the algorithms in several Python projects designed for different practical applications. You will expand your statistical knowledge to not only include a list of methods, but also the mathematical principles that link these methods together, equipping you with the tools you need to develop new ones.
armandgrillet / StscA implementation of the Self-Tuning Spectral Clustering algorithm, and more.
frknayk / FuzzyPID GeneticAlgorithmTuning fuzzy PID controller with genetic algorithm
Tokasumi / CS303 Fall2021 Reversed ReversiGenetic Algorithm based hyperparameter tuning tools for Reversi gaming AI (MIninax search with alpha-beta pruning)
Cheejyg / Genetic Algorithms For Swarm Parameter TuningSwarming behaviour is based on aggregation of simple drones exhibiting basic instinctive reactions to stimuli. However, to achieve overall balanced/interesting behaviour the relative importance of these instincts, as well their internal parameters, must be tuned. In this project, you will learn how to apply Genetic Programming as means of such tuning, and attempt to achieve a series of non-trivial swarm-level behaviours.
ooorin / Auto Tuning PIDa fuzzy expert system for auto-tuning PID algorithm
