AdaBoost | Small and easy C AdaBoost Implementation

by astromme C++ Version: Current License: No License

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kandi X-RAY | AdaBoost Summary

AdaBoost is a C++ library. AdaBoost has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

Small and easy C++ AdaBoost Implementation

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AdaBoost has a low active ecosystem.

It has 61 star(s) with 43 fork(s). There are 10 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 2 have been closed. On average issues are closed in 5 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of AdaBoost is current.

Quality

AdaBoost has no bugs reported.

Security

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

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AdaBoost 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.

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

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Community Discussions

Trending Discussions on AdaBoost

using random forest as base classifier with adaboost

Why's there a difference in prediction result between AdaBoost with n_estimators=1 that uses SVC as a base estimator, and just SVC

select Important-feature with Adaboost in python

How to use VIF in r?

Getting Error in running gbm from caret: Error in { : task 1 failed - "inputs must be factors"

Error "NameError: name 'self' is not defined" even though I declare "self"

Multi Classification Ensemble Cross validation Function too many values to unpack (expected 2)

adaBoost voting data and target form in python

Using scikit-learn's MLPClassifier in AdaBoostClassifier

Jupyter notebook cell is not printing anything

QUESTION

using random forest as base classifier with adaboost

Asked 2021-Jun-06 at 12:54

Can I use AdaBoost with random forest as a base classifier? I searched on the internet and I didn't find anyone who does it.

Like in the following code; I try to run it but it takes a lot of time:

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ANSWER

Answered 2021-Apr-07 at 11:30

No wonder you have not actually seen anyone doing it - it is an absurd and bad idea.

You are trying to build an ensemble (Adaboost) which in itself consists of ensemble base classifiers (RFs) - essentially an "ensemble-squared"; so, no wonder about the high computation time.

But even if it was practical, there are good theoretical reasons not to do it; quoting from my own answer in Execution time of AdaBoost with SVM base classifier:

Adaboost (and similar ensemble methods) were conceived using decision trees as base classifiers (more specifically, decision stumps, i.e. DTs with a depth of only 1); there is good reason why still today, if you don't specify explicitly the base_classifier argument, it assumes a value of DecisionTreeClassifier(max_depth=1). DTs are suitable for such ensembling because they are essentially unstable classifiers, which is not the case with SVMs, hence the latter are not expected to offer much when used as base classifiers.

On top of this, SVMs are computationally much more expensive than decision trees (let alone decision stumps), which is the reason for the long processing times you have observed.

The argument holds for RFs, too - they are not unstable classifiers, hence there is not any reason to actually expect performance improvements when using them as base classifiers for boosting algorithms, like Adaboost.

Source https://stackoverflow.com/questions/66977025

QUESTION

Why's there a difference in prediction result between AdaBoost with n_estimators=1 that uses SVC as a base estimator, and just SVC

Asked 2021-Mar-17 at 13:54

I am currently using daily financial data to fit my SVM and AdaBoost. To check my result, I tried AdaBoost with n_estimators=1 so that it would return same result as I just run a single SVM.

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ANSWER

Answered 2021-Mar-17 at 07:59

You haven't done anything wrong. The classifier sets a new random state every time you run it. To fix that just set the random_state parameter to any value you like.

Eg:

Source https://stackoverflow.com/questions/66668966

QUESTION

select Important-feature with Adaboost in python

Asked 2021-Feb-11 at 18:46

I want to select Important feature with adaboost. I found 'yellowbrick.model_selection' is very good and fast for this work. and I used this code. but it has problem. "ValueError: could not broadcast input array from shape (260200) into shape (1)
My feature vector has 1*260200 for every Image. I can't Underestand How adaboost make a model, so I can't debug the code. would you help me please? thank you a lot :)

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ANSWER

Answered 2021-Feb-11 at 18:46

this code, make a rank for every feature

Source https://stackoverflow.com/questions/66096706

QUESTION

How to use VIF in r?

Asked 2020-Oct-30 at 12:24

I am new in R and learning ml using caret. I was working on UCI bank marketing response data but used iris data here for reproducibility.

Issue is that I am getting error on running vif from car package on classification models.

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ANSWER

Answered 2020-Oct-29 at 14:27

car::vif is a function that needs to be adapted for each type of model. It works in the linked question because car::vif has been implemented to cope with glm models. car::vif does not support your chosen model type: gbm.

Source https://stackoverflow.com/questions/64592303

QUESTION

Getting Error in running gbm from caret: Error in { : task 1 failed - "inputs must be factors"

Asked 2020-Oct-27 at 14:02

I am new in R and trying to learn & execute ml in r.

I am getting this error on running gbm from caret : Error in { : task 1 failed - "inputs must be factors".

With the same parameters it ran perfectly for many other algos like - rf, adaboost etc.

Code for reference:

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ANSWER

Answered 2020-Oct-27 at 14:02

It seems like you are doing classification, if so, the distribution should be "bernoulli" instead of "gaussian", below is an example:

Source https://stackoverflow.com/questions/64554936

QUESTION

Error "NameError: name 'self' is not defined" even though I declare "self"

Asked 2020-Oct-02 at 08:42

I'm coding the AdaBoost from scratch in Python. Could you please elaborate on why the line self.functions[0] = f_0 causes an error?

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ANSWER

Answered 2020-Oct-02 at 08:23

I think that the reason for you error is that you cannot use self inside a class outside the methods, since, in order to use self an instance of the class have to be passed as a parameter to some function.

Notice that until you initialize your class, there’s no meaning for the expression self.

Source https://stackoverflow.com/questions/64168242

QUESTION

Multi Classification Ensemble Cross validation Function too many values to unpack (expected 2)

Asked 2020-Jul-10 at 23:22

[Link to SampleFile][1] [1]: https://www.dropbox.com/s/vk0ht1bowdhz85n/StackoverFlow_Example.csv?dl=0

Code below is in 2 parts Function and main code that calls function. There are a bunch of print statements along the way to help troubleshoot. I believe the issue has to do with the "mean_feature_importances" variable. This procedure works and does the comparison of binary classifiers with no issues. I have tried to change it to evaluate multi-class classifiers so I compare there performance. It makes sense why it expects only 2 labels because that is what it was for but this model has 5 different labels to choice from. I have changed every single value I think should be changed to accommodate 5 different labels instead of 2. Please advise if I missed something the issue happens on the return after print(19)

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ANSWER

Answered 2020-Jul-10 at 23:22

Depending on a condition, your function train_MultiClass_classifier_ensemble_CV returns either 2 or 3 arguments. Don't do that. Because when you want to assign the returned variables, there can be a mismatch. Now, it's returning 3 values but you want to assign that to only two values. Here's the problematic part:

Source https://stackoverflow.com/questions/62843488

QUESTION

adaBoost voting data and target form in python

Asked 2020-May-27 at 08:22

I'm trying to test this implementation of a voting adaBoost classifier.

My data set has the form of 650 triplets G1, G2, G3 where G1 and G2 are contained in [1-20] and G3 is either 1 or 0 based on G1 and G2.

From what I've read cross_val_score splits the input data in training and test groups by itself but i'm doing the X,y initialization wrong. If i try to initialize X and y with the whole data set the accuracy is 100% which seems a bit off.

I've tried to put only the G3 value in y, but i got the same result.

Normally i split the data into training and testing sets and that makes things easier.

I don't have much experience with python or machine learning, but i decided to give it a try.

Could you please explain what X and y initialization should look like for this to work properly?

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ANSWER

Answered 2020-May-27 at 08:22

You should remove G3 column from you X variable as this is what you're trying to predict.

Source https://stackoverflow.com/questions/62020206

QUESTION

Using scikit-learn's MLPClassifier in AdaBoostClassifier

Asked 2020-May-11 at 15:07

For a binary classification problem I want to use the MLPClassifier as the base estimator in the AdaBoostClassifier. However, this does not work because MLPClassifier does not implement sample_weight, which is required for AdaBoostClassifier (see here). Before that, I tried using a Keras model and the KerasClassifier within AdaBoostClassifier but that did also not work as mentioned here .

A way, which is proposed by User V1nc3nt is to build an own MLPclassifier in TensorFlow and take into account the sample_weight.

User V1nc3nt shared large parts of his code but since I have only limited experience with Tensorflow, I am not able to fill in the missing parts. Hence, I was wondering if anyone has found a working solution for building Adaboost ensembles from MLPs or can help me out in completing the solution proposed by V1nc3nt.

Thank you very much in advance!

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ANSWER

Answered 2020-May-11 at 15:07

Based on the references, which you had mentioned, I have modified MLPClassifier to accommodate sample_weights.

Try this!

Source https://stackoverflow.com/questions/55632010

QUESTION

Jupyter notebook cell is not printing anything

Asked 2020-Apr-08 at 08:07

I'm working on some ML classification problem on jupyter notebook. consider following code

Code (cell 1) ...

ANSWER

Answered 2020-Apr-08 at 08:07

thanks to @knoop , I zipped the names, classifier in final cell and that solved my problem.

Source https://stackoverflow.com/questions/61067526

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