Sold by: MphasisÂ
Deployed on AWS
The solution runs user specified feature selection tasks on input data and provides relevant features as output.
Overview
The solution runs machine learning related feature selection operations on the input data. This will simplify the task of feature selection for a data scientist where the user will have to specify few selected parameters to generate the correct output instead of writing specific code for each individual feature selection tasks.
Highlights
- This solution will provide the relevant and optimal features after running the user specified feature selection operations.
- This solution saves a significant amount of time spent over developing and running different feature selection operations on the user data.
- PACE - ML is Mphasis Framework and Methodology for end-to-end machine learning development and deployment. PACE-ML enables organizations to improve the quality & reliability of the machine learning solutions in production and helps automate, scale, and monitor them. Need customized Machine Learning and Deep Learning solutions? Get in touch!
Details
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Delivery method
Latest version
Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $20.00 |
ml.t2.medium Inference (Real-Time) Recommended | Model inference on the ml.t2.medium instance type, real-time mode | $10.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $20.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $20.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $20.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $20.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $20.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $20.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $20.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $20.00 |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
Legal
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
It is version 1.8 of the solution
Additional details
Inputs
- Summary
This algorithm takes a zip file as an input. This zip file should contain exactly two files:
- Data.csv – this will be the data on which feature selection is to be done
- Config.json – This file should contain parameters specific to feature selection tasks to be executed on the supplied data. The parameters of this file are explained further below:
- Input MIME type
- application/zip
https://github.com/Mphasis-ML-Marketplace/PACE-ML-Feature-Selection/tree/main/input
https://github.com/Mphasis-ML-Marketplace/PACE-ML-Feature-Selection/tree/main/input
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
Target_variable | Specify the target variable from the input dataset | Type: FreeText | Yes |
Apply_feature_selection | If feature selection should be applied on the provided data | Type: Categorical
Allowed values: True, False | Yes |
Feature_selections_top_features_to_keep | Can be a value between 0 to 1 | Type: Continuous
Minimum: 0
Maximum: 1 | Yes |
Feature_selection_method | Which feature selection method to use | Type: Categorical
Allowed values: classic, boruta | Yes |
Remove_multicollinearity | Whether multicollinearity should be removed from the data | Type: Categorical
Allowed values: True, False | Yes |
Maximum_correlation | Sets the threshold for features to drop while using remove multicollinearity function | Type: Continuous
Minimum: 0
Maximum: 1 | Yes |
Apply_pca | Whether PCA should be applied on the provided data | Type: Categorical
Allowed values: True, False | Yes |
Pca_method | Can be one of: linear, kernel, incremental | Type: Categorical
Allowed values: linear, kernel, incremental | Yes |
Pca_variance_retained_or_number_of_components | Specifies the amount of variance retained | Type: Continuous
Minimum: 0
Maximum: 1 | Yes |
Resources
Vendor resources
Support
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Customer reviews
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1 external reviews
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Anish Mahesh S.
Feature selection probably the most daunting task in ML journey
Reviewed on Jun 28, 2023
Review provided by G2
What do you like best about the product?
Feature selection is probably the most daunting task in a ML journey. Especially in an industrial setting there are a plethora of features which maybe higly co-related to each other thus making the life of an industrial data scientist the most difficult one but alsas there is a solution for this and thats 'Feature Selection for Machine Learning'.
What do you dislike about the product?
Making this solution more interpretable and explainable may be a challenge but with further iterations this is always possible. The UI could get a big overhaul too.
What problems is the product solving and how is that benefiting you?
Feature selection is always a conundrum for data scientist as there is always one too many faetures which doesn not play any vitalrole for the model output. As they say only a handful of soldiers are enough to wage a war.