simple script for plotting precision recall curves
-
Updated
Oct 29, 2017 - Python
simple script for plotting precision recall curves
This repository contains codes for running naive bayes and k-NN classification algorithms on large dataset in python
A visual analytics environment for supervised text classification and model evaluation.
Machine Learning/Pattern Recognition Models to analyze and predict if a client will subscribe for a term deposit given his/her marketing campaign related data
Sample project of fraud detection using Machine-Learning algorithms and Mathematical tools (roc)
Building a convolutional neural network for MNIST dataset, including hyperparameter search, dropout regularization, and optimizer selection
Used the Global Terrorism Database to Explore Features of Suicide Bombings
calculate ROC curve and find threshold for given accuracy
Analysing the telecom customer churn data
The data is related with direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe a term deposit (variable y).
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.
Recurrent Capsule Network for Text Classification
This is a classification problem where we have to determine either the employ of a company will leave or not.
A wide variety of supervised and unsupervised machine learning methods using the scikit-learn library
Estimated probability models that help target consumers to build brand loyalty
Bayesian approach for combined Particle Identification
Evaluate Machine Learning Models with Yellowbrick
Evaluation of Machine Learning Models with Yellowbrick
Predict fraudulent credit card transactions using TensorFlow, Keras, K Neighbors, Decision Tree, SVM Regression and Logistic Regression classifiers .
Add a description, image, and links to the roc-auc topic page so that developers can more easily learn about it.
To associate your repository with the roc-auc topic, visit your repo's landing page and select "manage topics."