Predict Breast Cancer Wisconsin (Diagnostic) using Naive Bayes

Last update: Apr 06, 2022

Overview

Naive-Bayes

Predict Breast Cancer Wisconsin (Diagnostic) using Naive Bayes

Downloading Data Set

Use our Breast Cancer Wisconsin Data Set
Also you can use this link to download the Breast Cancer Wisconsin (Diagnostic) Data Set: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)

Owner

Faeze Habibi

📫 How to reach me: [email protected]

GitHub Repository

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Predict Breast Cancer Wisconsin (Diagnostic) using Naive Bayes

Related tags

Overview

Naive-Bayes

Downloading Data Set

Owner

Faeze Habibi

End-to-End Dense Video Captioning with Parallel Decoding (ICCV 2021)

PyTorch implementation of PP-LCNet

This is the repository for the paper "Have I done enough planning or should I plan more?"

Node Editor Plug for Blender

Statsmodels: statistical modeling and econometrics in Python

CIFAR-10 Photo Classification

Official implementation of the paper: "LDNet: Unified Listener Dependent Modeling in MOS Prediction for Synthetic Speech"

Realtime Face Anti Spoofing with Face Detector based on Deep Learning using Tensorflow/Keras and OpenCV

A Transformer-Based Siamese Network for Change Detection

Face Alignment using python

Yet Another Reinforcement Learning Tutorial

This initial strategy was developed specifically for larger pools and is based on taking a moving average and deriving Bollinger Bands to create a projected active liquidity range.

imbalanced-DL: Deep Imbalanced Learning in Python

ByteTrack超详细教程！训练自己的数据集&&摄像头实时检测跟踪

Combine Tacotron2 and Hifi GAN to generate speech from text

PyTorch common framework to accelerate network implementation, training and validation

This repository allows you to anonymize sensitive information in images/videos. The solution is fully compatible with the DL-based training/inference solutions that we already published/will publish for Object Detection and Semantic Segmentation.

OSLO: Open Source framework for Large-scale transformer Optimization

The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.

pyhsmm - library for approximate unsupervised inference in Bayesian Hidden Markov Models (HMMs) and explicit-duration Hidden semi-Markov Models (HSMMs), focusing on the Bayesian Nonparametric extensions, the HDP-HMM and HDP-HSMM, mostly with weak-limit approximations.