Evaluate on three different ML model for feature selection using Breast cancer data.

Related tags

Machine LearningAnomaly-detection-Feature-Selection
Overview

Anomaly-detection-Feature-Selection

Evaluate on three different ML model for feature selection using Breast cancer data.

ML models: SVM, KNN and MLP.

Report recall and Precision of each model.

Data

Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. n the 3-dimensional space is that described in: [K. P. Bennett and O. L. Mangasarian: "Robust Linear Programming Discrimination of Two Linearly Inseparable Sets", Optimization Methods and Software 1, 1992, 23-34]

More details about data : https://www.kaggle.com/uciml/breast-cancer-wisconsin-data

Owner
Tarek idrees
Bioinformatics Engineer👨‍🔬
Tarek idrees
GitHub Repository
DirectML is a high-performance, hardware-accelerated DirectX 12 library for machine learning.

DirectML is a high-performance, hardware-accelerated DirectX 12 library for machine learning. DirectML provides GPU acceleration for common machine learning tasks across a broad range of supported ha

Microsoft 1.1k Jan 04, 2023
XAI - An eXplainability toolbox for machine learning

XAI - An eXplainability toolbox for machine learning XAI is a Machine Learning library that is designed with AI explainability in its core. XAI contai

The Institute for Ethical Machine Learning 875 Dec 27, 2022
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

Machine Learning Notebooks, 3rd edition This project aims at teaching you the fundamentals of Machine Learning in python. It contains the example code

Aurélien Geron 1.6k Jan 05, 2023
Lightweight Machine Learning Experiment Logging 📖

Simple logging of statistics, model checkpoints, plots and other objects for your Machine Learning Experiments (MLE). Furthermore, the MLELogger comes with smooth multi-seed result aggregation and co

Robert Lange 65 Dec 08, 2022
Transform ML models into a native code with zero dependencies

m2cgen (Model 2 Code Generator) - is a lightweight library which provides an easy way to transpile trained statistical models into a native code

Bayes' Witnesses 2.3k Jan 03, 2023
Bodywork deploys machine learning projects developed in Python, to Kubernetes.

Bodywork deploys machine learning projects developed in Python, to Kubernetes. It helps you to: serve models as microservices execute batch jobs run r

Bodywork Machine Learning 409 Jan 01, 2023
Gaussian Process Optimization using GPy

End of maintenance for GPyOpt Dear GPyOpt community! We would like to acknowledge the obvious. The core team of GPyOpt has moved on, and over the past

Sheffield Machine Learning Software 847 Dec 19, 2022
Evaluate on three different ML model for feature selection using Breast cancer data.

Anomaly-detection-Feature-Selection Evaluate on three different ML model for feature selection using Breast cancer data. ML models: SVM, KNN and MLP.

Tarek idrees 1 Mar 17, 2022
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

DoWhy | An end-to-end library for causal inference Amit Sharma, Emre Kiciman Introducing DoWhy and the 4 steps of causal inference | Microsoft Researc

Microsoft 5.6k Jan 07, 2023
Greykite: A flexible, intuitive and fast forecasting library

The Greykite library provides flexible, intuitive and fast forecasts through its flagship algorithm, Silverkite.

LinkedIn 1.4k Jan 15, 2022
Nevergrad - A gradient-free optimization platform

Nevergrad - A gradient-free optimization platform nevergrad is a Python 3.6+ library. It can be installed with: pip install nevergrad More installati

Meta Research 3.4k Jan 08, 2023
Apache (Py)Spark type annotations (stub files).

PySpark Stubs A collection of the Apache Spark stub files. These files were generated by stubgen and manually edited to include accurate type hints. T

Maciej 114 Nov 22, 2022
Predict the demand for electricity (R) - FRENCH

06.demand-electricity Predict the demand for electricity (R) - FRENCH Prédisez la demande en électricité Prérequis Pour effectuer ce projet, vous devr

1 Feb 13, 2022
pywFM is a Python wrapper for Steffen Rendle's factorization machines library libFM

pywFM pywFM is a Python wrapper for Steffen Rendle's libFM. libFM is a Factorization Machine library: Factorization machines (FM) are a generic approa

João Ferreira Loff 251 Sep 23, 2022
Scikit learn library models to account for data and concept drift.

liquid_scikit_learn Scikit learn library models to account for data and concept drift. This python library focuses on solving data drift and concept d

7 Nov 18, 2021
Hierarchical Time Series Forecasting using Prophet

htsprophet Hierarchical Time Series Forecasting using Prophet Credit to Rob J. Hyndman and research partners as much of the code was developed with th

Collin Rooney 131 Dec 02, 2022
Traingenerator 🧙 A web app to generate template code for machine learning ✨

Traingenerator 🧙 A web app to generate template code for machine learning ✨ 🎉 Traingenerator is now live! 🎉

Johannes Rieke 1.2k Jan 07, 2023
BASTA: The BAyesian STellar Algorithm

BASTA: BAyesian STellar Algorithm Current stable version: v1.0 Important note: BASTA is developed for Python 3.8, but Python 3.7 should work as well.

BASTA team 16 Nov 15, 2022
Scikit-learn compatible wrapper of the Random Bits Forest program written by (Wang et al., 2016)

sklearn-compatible Random Bits Forest Scikit-learn compatible wrapper of the Random Bits Forest program written by Wang et al., 2016, available as a b

Tamas Madl 8 Jul 24, 2021
A Python library for choreographing your machine learning research.

A Python library for choreographing your machine learning research.

AI2 270 Jan 06, 2023