There are 4 repositories under parameter-tuning topic.
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
(Deprecated) Scikit-learn integration package for Apache Spark
LAMA - automatic model creation framework
A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models.
An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.
Hyperparameter optimization in Julia.
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
Workflow engine for exploration of simulation models using high throughput computing
Forecast stock prices using machine learning approach. A time series analysis. Employ the Use of Predictive Modeling in Machine Learning to Forecast Stock Return. Approach Used by Hedge Funds to Select Tradeable Stocks
Alchemy Cat —— 🔥Config System for SOTA
An abstraction layer for parameter tuning
A Python Toolkit for Managing a Large Number of Experiments
Understand the relationships between various features in relation with the sale price of a house using exploratory data analysis and statistical analysis. Applied ML algorithms such as Multiple Linear Regression, Ridge Regression and Lasso Regression in combination with cross validation. Performed parameter tuning, compared the test scores and suggested a best model to predict the final sale price of a house. Seaborn is used to plot graphs and scikit learn package is used for statistical analysis.
Machine Learning Project using Kaggle dataset
Trying PostgreSQL parameter tuning using machine learning.
Learning simulation parameters from experimental data, from the micro to the macro, from laptops to clusters.
Algorithm Configuration Visualizations for irace!
MATLAB simulation of a BPSK data transmission system with AWGN channel, and its benchmark against BER(SNR).
Robustness of DWT vs DCT is graded based on the quality of extracted watermark. The measure used is the Correlation coefficient (0-100%).
Swarming 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.
The performance of SVR models highly depends upon the appropriate choice of SVR parameters. Here, different metaheuristic algorithms are used to tune the hyperparameters.
Online Hackathons/Competitions
The goal of this project is to design a classifier to use for sentiment analysis of product reviews. Our training set consists of reviews written by Amazon customers for various food products. The reviews, originally given on a 5 point scale, have been adjusted to a +1 or -1 scale, representing a positive or negative review, respectively.
The project has text vectorization, handling big data with merging and cleaning the text and getting the required columns while boosting the performance by feature extraction and parameter tuning for NN, compares the Performances through applied different models treating the problem as classification and regression both.
Codes and templates for ML algorithms created, modified and optimized in Python and R.
a case study on deep learning where tuning simple SVM is much faster and better than CNN
MPS-APO is a rapid and automatic parameter optimizer for multiple-point geostatistics
It is a Problem Which I got During the ZS Data Science Challenge From Interview Bit Hiring Challenge Where I secured a 40th Rank out of 10,000 Students across India. It is a Dataset which requires Intensive Cleaning and Processing. Here I have Performed Classification Using Random Forest Classifier and Used Hyper Tuning of the Parameters to achieve the Accuracy. I got a very Satisfiable Accuracy from the Model in both the Training and Testing Sets.
An RShiny dashboard for visualisation of mass spectrometry (MS) data and fine-tuning of xcms pre-processing parameters