There are 0 repository under hyper-parameter-tuning topic.
DataFrame support for scikit-learn.
Nature-inspired algorithms for hyper-parameter tuning of Scikit-Learn models.
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.
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 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.
This is Project which contains Data Visualization, EDA, Machine Learning Modelling for Checking the Sentiments.
Convenient classes for optimizing Hyper-parameters, using Random search, Spearmint and SigOpt
Graded assignments of all the courses that are being offered in Coursera Deep Learning Specialization by DeepLearning.AI. (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Network (v) Squence Model
Combined hyper-parameter optimization and feature selection for machine learning models using micro genetic algorithms
Text classification with Machine Learning and Mealpy
Hyper-parameter tuning of classification model with Mealpy
Hyper-parameter tuning of Time series forecasting models with Mealpy
Surrogate adaptive randomized search for hyper-parameters tuning in sklearn.
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 1-year period to avoid any tendency to a specific campaign, special day, user profile, or period.
Data visualization, hypothesis testing and song recommendation with Python
A simple python interface for running multiple parallel instances of a python program (e.g. gridsearch).
Predicting if it will rain the next day with clustering and supervised ML
Performance predictor with learning curves and meta-features
Flight fare perdicting model
CLI to create and optimize optuna study without explicit objective function
Modeling of strength of high performance concrete using Machine Learning
Visualized the activations of hidden layers, analyzed feature invariance due to different image alterations and the effects of change in filter-sizes and strides
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 their demographic and socio-economic characteristics used in this study. The Source for the data-set is the UCI Machine Learning Repository at, http://http://archive.ics.uci.edu/ml/datasets/Contraceptive+Method+Choice [?]. This study consists of two phases. The objective of Phase I is to preprocess and explore the data-set in order to build the model in Phase II. All the activities have been performed in the Python package in this study and Compiled from Jupyter Notebook This report covers both narratives and the Python pseudocodes for the data preprocessing and exploration performed under phase I. Content of this report is organized as follows. Section 1 describes the data sets and their attributes. Section 2 covers data preprocessing. In Section 3, each attribute and its inter-relationships are explored.
Using Facebook Adaptive Experimentation platform to tune random forest regressors using docker
The used cars price is predicted using various features - Decision Tree & Random Forest
Hyper-Parameter Optimisation experiment as part of my undergraduate dissertation (2019)