Classification with imbalanced classes
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Updated
Jun 14, 2018 - Jupyter Notebook
Classification with imbalanced classes
Machine learning model for Credit Card fraud detection
Using SageMaker's linear classifier to detect fraud. Addressing class imbalance and setting target metrics for Precision and Recall
Class imbalance correction algorithm for multiple-instance data
Sampling-based class imbalance solutions for multiple-instance classification
PREDICTING A PULSAR STAR - A Classic Class Imbalance Problem
Evaluate Machine Learning Models with Yellowbrick
Most existing classification approaches assume the underlying training set is evenly distributed but many real-world classification problems have an imbalanced class distribution, such as rare disease identification, fraud detection, spam detection, churn prediction, electricity theft & pilferage etc.
Predict loan approval by using different variable selection methods
A collection of machine learning mini-projects.
Predict whether customers of a bank will subscribe to a term deposit and analyze customer behaviour based on the bank's historical telemarketing campaign records.
Machine Learning model for Credit Card Fraud Detection
A credit card fraud detection kernel
jBVQ: Bayes Vector Quantizer for Java
To deal with the class imbalance problem in multi-label learning with missing labels, we propose Class Imbalance aware Missing labels Multi-label Learning, CIMML. Our proposed method handles class imbalance issue by constructing a label weight matrix with weight estimation guided by how frequently a label is present, absent, and unobserved.
Human or Robot? Predict if an online bid is made by a machine or a human.
Binary classification, with every feature as categoricals
Credit Card Fraud Detection using Machine Learning
Advanced NER Applications: Implementing KNN, Feed-Forward, and LSTM Models with Class Imbalance Reduction Techniques.
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