What is cumulative gain curve in ML?

This recipe explains what is cumulative gain curve in ML

Recipe Objective

What is cumulative gain curve in ML?

The cumulative gain curve evaluates the performance of the model by comparing the results of a random pick with the model. The graph shows the percentage of targets reached while considering a certain percentage of the population with the highest probability to be target according to the model.

How cumulative gain chart is made.

1.) tpr - True Positive Rate or Sensitivity tpr=TP/(TP+FN)

2.) sup - Support (Predictive Positive Rate) sup = (TP+FP)/N = (predicted pos)/total fraction of positively predicted examples

3.) recall 4.) Accuracy

Gain Chart

* True Positive Rate vs Predicted Positive Rate.

What Users are saying..

profile image

Anand Kumpatla

Sr Data Scientist @ Doubleslash Software Solutions Pvt Ltd
linkedin profile url

ProjectPro is a unique platform and helps many people in the industry to solve real-life problems with a step-by-step walkthrough of projects. A platform with some fantastic resources to gain... Read More

Relevant Projects

NLP Project to Build a Resume Parser in Python using Spacy
Use the popular Spacy NLP python library for OCR and text classification to build a Resume Parser in Python.

Learn Hyperparameter Tuning for Neural Networks with PyTorch
In this Deep Learning Project, you will learn how to optimally tune the hyperparameters (learning rate, epochs, dropout, early stopping) of a neural network model in PyTorch to improve model performance.

Deep Learning Project for Time Series Forecasting in Python
Deep Learning for Time Series Forecasting in Python -A Hands-On Approach to Build Deep Learning Models (MLP, CNN, LSTM, and a Hybrid Model CNN-LSTM) on Time Series Data.

Build Multi Class Text Classification Models with RNN and LSTM
In this Deep Learning Project, you will use the customer complaints data about consumer financial products to build multi-class text classification models using RNN and LSTM.

Ola Bike Rides Request Demand Forecast
Given big data at taxi service (ride-hailing) i.e. OLA, you will learn multi-step time series forecasting and clustering with Mini-Batch K-means Algorithm on geospatial data to predict future ride requests for a particular region at a given time.

Loan Default Prediction Project using Explainable AI ML Models
Loan Default Prediction Project that employs sophisticated machine learning models, such as XGBoost and Random Forest and delves deep into the realm of Explainable AI, ensuring every prediction is transparent and understandable.

AWS MLOps Project for ARCH and GARCH Time Series Models
Build and deploy ARCH and GARCH time series forecasting models in Python on AWS .

Build Portfolio Optimization Machine Learning Models in R
Machine Learning Project for Financial Risk Modelling and Portfolio Optimization with R- Build a machine learning model in R to develop a strategy for building a portfolio for maximized returns.

Abstractive Text Summarization using Transformers-BART Model
Deep Learning Project to implement an Abstractive Text Summarizer using Google's Transformers-BART Model to generate news article headlines.

Deep Learning Project- Real-Time Fruit Detection using YOLOv4
In this deep learning project, you will learn to build an accurate, fast, and reliable real-time fruit detection system using the YOLOv4 object detection model for robotic harvesting platforms.