XGBoost Model

A business user could use this bot to perform decision tree-based ensemble classification which is based on principle of gradient boosting framework i.e. XGBoost.

Top Benefits

  • Increase Productivity: Improves Predictions process time by 40%.
  • Process Simplification: Reduced time with complex risk scoring multiple calculations and provides quick decisions.
  • Quantitative Decision Support: Provides high level of accuracy based on quality of input data parameters.
  • Zero Maintenance: Model is provided as SaaS using serverless function architecture of Azure.

Tasks

  • XGBoost is an ensemble tree method that apply the principle of boosting weak learners.
  • This bot could be used to solve classification and user-defined analytics problems.
  • XGBoost has in-built regularization which prevents the model from overfitting.
  • XGBoost has in-built cross validation capability.
  • The Bot takes in the input data in CSV file and performs the XGBoost and provides output in the MS excel.

Inputs:
Path: Path of the CSV file Ex.: C:~Life expentency data.csv
NoOfColumn: Number of Columns in the CSV file
InputColumn: Header of the input columns of the CSV file Ex: 'Country','Year','Status','Life expectancy','Adult Mortality','infant deaths','Alcohol','percentage expenditure','Hepatitis B','Measles','BMI','under-five deaths ','Polio','Total expenditure','Diphtheria','HIV/AIDS','GDP','Population','thinness 1-19 years','thinness 5-9 years','Income composition of resources','Schooling'
InputColumnDataType: Data Types of the input column (Available datatype: Numeric, String, Date) Ex: 'String','Numeric','String','Numeric','Numeric','Numeric','Numeric','Numeric','Numeric','Numeric','Numeric','Numeric','Numeric','Numeric','Numeric','Numeric','Numeric','Numeric','Numeric','Numeric','Numeric','Numeric'
MissingNumberValueReplacement: By which value you have to replace the missing number values
MissingStringValueReplacement: By what string you have to replace the missing string values
IgnoreColumns: header of the Columns which you have to ignore in the model.  Ex: 'Status'
PredictionColumnName: Header of the Column on which you want to predict Ex: 'Life Expentency'
Ex: This input examples states the life expectancy of the person from all over the world

Outputs:
Output: the result in the form of the MS Excel
Provides predictions on desired parameters
Ex: The required output predicts the life expectancy of the person using the XGBoost model baes on below class:

○ Gold_Class: This column is taken as the predicted target
○ Predicted_Class: This tells us the predicted age of the person

Get Bot

Free

Bot Security Program
Level 1
Applications
Business Process
Category
Vendor
Automation Type
Bot
Last Updated
July 24, 2020
First Published
June 26, 2020
Enterprise Version
11.x
Community Version
11.3.1
ReadMe
ReadMe
Support

See the Bot in Action

Code
Input Sample
Output Sample
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Code
Input Sample
Output Sample

Setup Process

Install

Download the Bot and follow the instructions to install it in your AAE Control Room.

Configure

Open the Bot to configure your username and other settings the Bot will need (see the Installation Guide or ReadMe for details.)

Run

That's it - now the Bot is ready to get going!

Requirements and Inputs

  • Path: Path of the CSV file.
  • NoOfColumn: Number of Columns in the CSV file.
  • InputColumn: Header of the input columns of the CSV file.
  • InputColumnDataType: Data Types of the input column (Available datatype: Numeric, String, Date).
  • MissingNumberValueReplacement: By which value you have to replace the missing number values.
  • MissingStringValueReplacement: By what string you have to replace the missing string values.
  • IgnoreColumns: header of the Columns which you have to ignore in the model.
  • PredictionColumnName: Header of the Column on which you want to predict.