Convenient classes for optimizing Hyper-parameters, using Random search, Spearmint and SigOpt
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Updated
Sep 3, 2017 - Jupyter Notebook
Convenient classes for optimizing Hyper-parameters, using Random search, Spearmint and SigOpt
Combined hyper-parameter optimization and feature selection for machine learning models using micro genetic algorithms
Efficient and Scalable Batch Bayesian Optimization Using K-Means
This is Data set to Classify the Benign and Malignant cells in the given data set using the description about the cells in the form of columnar attributes. There are Visualizations and Analysis for Support.
In this data set, We have to predict the patients who are most likely to suffer from cervical cancer using Machine Learning algorithms for Classifications, Visualizations and Analysis.
This is Project which contains Data Visualization, EDA, Machine Learning Modelling for Checking the Sentiments.
Hyper-Parameter Optimisation experiment as part of my undergraduate dissertation (2019)
Using Facebook Adaptive Experimentation platform to tune random forest regressors using docker
Using Machine Learning Algorithms for Regression Analysis to predict the sales pattern and Using Data Analysis and Data Visualizations to Support it.
In this data set we have perform classification or clustering and predict the intention of the Online Customers Purchasing Intention. The data set was formed so that each session would belong to a different user in a 1-year period to avoid any tendency to a specific campaign, special day, user profile, or period.
Surrogate adaptive randomized search for hyper-parameters tuning in sklearn.
Predicting if it will rain the next day with clustering and supervised ML
Modeling of strength of high performance concrete using Machine Learning
Performance predictor with learning curves and meta-features
Examples of parameter tuning via DrOpt.
The data used in this analysis is an Online Shoppers Purchasing Intention data set provided on the UC Irvine’s Machine Learning Repository. The primary purpose of the data set is to predict the purchasing intentions of a visitor to this particular store’s website. The data set was formed so that each session would belong to a different user in a…
Visualized the activations of hidden layers, analyzed feature invariance due to different image alterations and the effects of change in filter-sizes and strides
A simple python interface for running multiple parallel instances of a python program (e.g. gridsearch).
Predicting the Contraceptive Method Choice of a Woman Based on Demographic and Socio-economic Characteristics - The objective of this study is to to predict the contraceptive methods (no use, long-term methods, or short-term methods) of a woman based on her demographic and socio-economic characteristics. A data-set of 1473 married women with the…
Flight fare perdicting model
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