ensembler | Powerful stacking/blending ensemble implementation | Machine Learning library

by pankajr141 Python Version: 0.2 License: No License

X-Ray Key Features Code Snippets Community Discussions(4)Vulnerabilities Install Support

kandi X-RAY | ensembler Summary

ensembler is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. ensembler has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can install using 'pip install ensembler' or download it from GitHub, PyPI.

Powerful stacking/blending ensemble implementation in python.

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ensembler has a low active ecosystem.

It has 4 star(s) with 0 fork(s). There are 1 watchers for this library.

It had no major release in the last 12 months.

ensembler has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of ensembler is 0.2

Quality

ensembler has no bugs reported.

Security

ensembler has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

ensembler does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

ensembler releases are not available. You will need to build from source code and install.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed ensembler and discovered the below as its top functions. This is intended to give you an instant insight into ensembler implemented functionality, and help decide if they suit your requirements.

Train base classifiers
Perform Bayesian Optimization .
Fit the model to a dataset .
Compute the ensemble by weight loss .
Get extra tuning parameters .
Return parameters for logistic regression tuning .
Return default tuning parameters for XGB classifier .
Perform random search .
Performs a grid search optimizer .
Get the scaled data .

Get all kandi verified functions for this library.

ensembler Key Features

No Key Features are available at this moment for ensembler.

ensembler Examples and Code Snippets

No Code Snippets are available at this moment for ensembler.

Community Discussions

Trending Discussions on ensembler

Encoding categorical columns - Label encoding vs one hot encoding for Decision trees

RecursionError: maximum recursion depth exceeded when blending SKlearn Models

How do I change - using for loops to call multiple functions - into - using a pipeline to call a class?

In SAS, how to concatenate multiple rows into 1 by some ID

QUESTION

Encoding categorical columns - Label encoding vs one hot encoding for Decision trees

Asked 2020-Jun-06 at 23:48

The way decision trees and random forest work using splitting logic, I was under the impression that label encoding would not be a problem for these models, as we are anyway going to split the column. For eg: if we have gender as 'male', 'female' and 'other', with label encoding, it becomes 0,1,2 which is interpreted as 0<1<2. But since we are going to split the columns, I thought it didn't matter as it is the same thing whether we are going to split on 'male' or '0'. But when I tried both label and one hot encoding on the dataset, one hot encoding gave better accuracy and precision. Can you kindly share your thoughts.

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ANSWER

Answered 2020-Jun-06 at 20:48

You can see it as a regularization effect: your model is simpler, and so more generalizable. So you get better performances.

Taking your example of the sex feature: [male, female, other] with label encoding become [0, 1, 2].

Now suppose there is a particular configuration of the other features which works only for females: the tree needs two branches to select females, one which select sex bigger than zero, and the other which select sex lower than 2.

Instead, with one-hot encoding, you only need a branch to do the selection, say sex_female bigger than zero.

Source https://stackoverflow.com/questions/62237033

QUESTION

RecursionError: maximum recursion depth exceeded when blending SKlearn Models

Asked 2019-Sep-14 at 02:30

I'm trying to blend different models for SciKit learn so I can average their predictions.

This is Ensemble class that I've created:

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ANSWER

Answered 2019-Sep-14 at 02:30

Don't you mean something like this:

Source https://stackoverflow.com/questions/57931628

QUESTION

How do I change - using for loops to call multiple functions - into - using a pipeline to call a class?

Asked 2019-Apr-03 at 19:37

So the basic requirement is that, I get a dictionary of models from user, and a dictionary of their hyper parameters and give a report. Currently goal is for binary classification, but this can be extended later.

This is what I am currently doing:

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ANSWER

Answered 2019-Apr-03 at 19:37

I have implemented a working solution. I should have worded my question better. I initially misunderstood how GridsearchCV or RandomizedSearchCV works internally. cv_results_ gives all the results of the grid available. I thought only the best estimator was available to us.

Using this, for each type of model, I took the max rank_test_score, and got the parameters making up the model. In this example, it is 4 models. Now I ran each of those models, i.e. the best combination of parameters for each model, with my test data, and predicted the required scores. I think this solution can be extended to RandomizedSearchCV and a lot more other options.

NOTE: This is just a trivial solution. Lot of modifications necessary, like needing to scale data for specific models, etc. This solution will just serve as a starting point which can be modified according to the user's needs.

Credits to this answer for the ClfSwitcher() class.

Following is the implementation of the class (suggestions to improve are welcomed).

Source https://stackoverflow.com/questions/55468376

QUESTION

In SAS, how to concatenate multiple rows into 1 by some ID

Asked 2018-May-04 at 11:00

I have a table like this

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ANSWER

Answered 2018-May-03 at 20:15

Use SAS Retain functionality to concatenate the text and only output when an new org_ID is read.

Note: The two IF statements handles the cases of first row and last row; where there is no Previous ID or no Next ID.

Working Code: (Your Input data must be sorted)

Source https://stackoverflow.com/questions/50149807

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install ensembler

You can install using 'pip install ensembler' or download it from GitHub, PyPI.
You can use ensembler like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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