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Search results for parameter tuning

parameter-tuning x

22 search results found

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

Spark Sklearn ⭐ 1,039

(Deprecated) Scikit-learn integration package for Apache Spark

Lightautoml ⭐ 769

LAMA - automatic model creation framework

Automated modeling and machine learning framework FEDOT

Shap Hypetune ⭐ 496

A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models.

Hyperactive ⭐ 475

An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.

Hyperopt.jl ⭐ 191

Hyperparameter optimization in Julia.

Openmole ⭐ 141

Workflow engine for exploration of simulation models using high throughput computing

⚡ Fast Concurrent / Parallel Sorting in Go

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

Stock Return Prediction Using Knn Svm Guassian Process Adaboost Tree Regression And Qda ⭐ 85

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

Purely functional genetic algorithms for multi-objective optimisation

Framework of intelligent optimization methods iOpt

A Python Toolkit for Managing a Large Number of Experiments

An abstraction layer for parameter tuning

Acces Coexsist ⭐ 13

Learning simulation parameters from experimental data, from the micro to the macro, from laptops to clusters.

Postgres_opttune ⭐ 9

Trying PostgreSQL parameter tuning using machine learning.

Algorithm Configuration Visualizations for irace!

Watermark ⭐ 9

Robustness of DWT vs DCT is graded based on the quality of extracted watermark. The measure used is the Correlation coefficient (0-100%).

Capstoneproject_house_prices_prediction ⭐ 7

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 analysi

MATLAB simulation of a BPSK data transmission system with AWGN channel, and its benchmark against BER(SNR).

Easyoverhard ⭐ 5

a case study on deep learning where tuning simple SVM is much faster and better than CNN

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