Multi-class confusion matrix library in Python
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Updated
Sep 7, 2024 - Python
Multi-class confusion matrix library in Python
Confusion Matrix in Python: plot a pretty confusion matrix (like Matlab) in python using seaborn and matplotlib
Neo: Hierarchical Confusion Matrix Visualization (CHI 2022)
[Not Actively Maintained] Whitebox is an open source E2E ML monitoring platform with edge capabilities that plays nicely with kubernetes
Seamlessly integrate matplotlib figures as tensorflow summaries.
BEST SCORE ON KAGGLE SO FAR , EVEN BETTER THAN THE KAGGLE TEAM MEMBER WHO DID BEST SO FAR. The project is about diagnosing pneumonia from XRay images of lungs of a person using self laid convolutional neural network and tranfer learning via inceptionV3. The images were of size greater than 1000 pixels per dimension and the total dataset was tagg…
Measure and visualize machine learning model performance without the usual boilerplate.
Python class for calculating confusion matrix for object detection task
✋🏼🛑 This one stop project is a complete COVID-19 detection package comprising of 3 tasks: • Task 1 --> COVID-19 Classification • Task 2 --> COVID-19 Infection Segmentation • Task 3 --> Lung Segmentation
Detecting Frauds in Online Transactions using Anamoly Detection Techniques Such as Over Sampling and Under-Sampling as the ratio of Frauds is less than 0.00005 thus, simply applying Classification Algorithm may result in Overfitting
Google Street View House Number(SVHN) Dataset, and classifying them through CNN
Using Tensorflow and a Support Vector Machine to Create an Image Classifications Engine
Solutions to kdd99 dataset with Decision tree and Neural network by scikit-learn
Predicting air pollution level in a specific city
Confusion matrix metrics directly from your pandas DataFrame
Karma of Humans is AI
Reinforcement Learning with Perturbed Reward, AAAI 2020
Deep learning for freehand sketch object recognition
Confusion matrix for Mask R-CNN (Matterport implementation)
This toolbox offers 8 machine learning methods including KNN, SVM, DA, DT, and etc., which are simpler and easy to implement.
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