Latest release of Oracle Data Visualization has inbuilt Machine Learning features. This means users can now build their own models from training data and use these trained models for prediction and classification. Good news is that Oracle DV comes equipped with host of ML algorithms that can perform Numeric Prediction, Multi & Binary Classification and Clustering in addition to allowing your own custom model scripts for train & score.
In this blog we are going to focus on Binary classification algorithms and show how to use those inbuilt algorithms for addressing a real-life, common question for any organization: Predict Employee Attrition - i.e. find which employees are likely to quit.
Before we venture any further let us try to understand briefly what is Binary classification. Binary classification is a technique of classifying records/elements of a given dataset into two groups on the basis of classification rules for ex: Employee Attrition Prediction whether the employee is expected to Leave or Not Leave (Leave and Not Leave are two different groups).
These classification rules are generated when we train a model using training dataset which contains information about the employees and whether the employee has left the company or not. Oracle DV is shipped with multiple algorithms that can perform Binary classification. Here is a snapshot showing list of inbuilt algorithms in Oracle DV that can perform binary classification:
Users can also upload their own Python/R scripts(with appropriate tags) which can perform Binary classification and these custom algorithms will show up in the list and can be used for prediction.
Now let us see how one of these inbuilt algorithms can be used to predict Employee Attrition prediction i.e., whether the employee will leave or not i..e, Yes or No. This video explains process of model creation as well as prediction process (i.e. scoring using created model).
Example seen in the video can be downloaded from Oracle Analytics Store. Name of the project is Example DV project: Attrition Prediction:
In this blog we are going to focus on Binary classification algorithms and show how to use those inbuilt algorithms for addressing a real-life, common question for any organization: Predict Employee Attrition - i.e. find which employees are likely to quit.
Before we venture any further let us try to understand briefly what is Binary classification. Binary classification is a technique of classifying records/elements of a given dataset into two groups on the basis of classification rules for ex: Employee Attrition Prediction whether the employee is expected to Leave or Not Leave (Leave and Not Leave are two different groups).
These classification rules are generated when we train a model using training dataset which contains information about the employees and whether the employee has left the company or not. Oracle DV is shipped with multiple algorithms that can perform Binary classification. Here is a snapshot showing list of inbuilt algorithms in Oracle DV that can perform binary classification:
Users can also upload their own Python/R scripts(with appropriate tags) which can perform Binary classification and these custom algorithms will show up in the list and can be used for prediction.
Now let us see how one of these inbuilt algorithms can be used to predict Employee Attrition prediction i.e., whether the employee will leave or not i..e, Yes or No. This video explains process of model creation as well as prediction process (i.e. scoring using created model).
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6 comments:
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thanks for sharing wonderful article. Keep posting!
As we know, Binary classification is the base of whole computing, everyone must know this basic concept. Check out machine learning solutions to get all your answers about machine learning and AI.
Thanks for sharing your post. it is very helpful in machine learning solutions.
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