#Parameter-tuning

Showing 7 of 7 repositories tagged #parameter-tuning, ranked by stars

EpistasisLab
EpistasisLab
tpot

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

Score
100
★ 10.0k ⑂ 1.6k
Jupyter Notebook
sberbank-ai-lab
sberbank-ai-lab
LightAutoML

LAMA - automatic model creation framework

Score
0
★ 922 ⑂ 97
Python
aimclub
aimclub
FEDOT

Automated modeling and machine learning framework FEDOT

Score
0
★ 709 ⑂ 92 +2/day
Python
hyperactive-project
hyperactive-project
Hyperactive

A unified interface for optimization algorithms and experiments

Score
100
★ 551 ⑂ 73 +2/day
Python
EpistasisLab
EpistasisLab
tpot2

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

Score
50
★ 250 ⑂ 33
Jupyter Notebook
kennedyCzar
kennedyCzar
STOCK-RETURN-PREDICTION-USING-KNN-SVM-GUASSIAN-PROCESS-ADABOOST-TREE-REGRESSION-AND-QDA

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

Score
0
★ 137 ⑂ 35
Jupyter Notebook
NikhilaThota
NikhilaThota
CapstoneProject_House_Prices_Prediction

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.

Score
0
★ 27 ⑂ 16
Jupyter Notebook
Related Topics
#automated-machine-learning#machine-learning#automl#data-science#feature-engineering#hyperparameter-optimization#model-selection#python#automation#gradient-boosting#random-forest#scikit-learn

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