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You can easily achieve this result using map and filter.

Here’s a ‘tidyverse’ solution:

TL;DR

When the function DataLongGenerator() creates Z, it's a random uniform data in a matrix. The actual coding is

There are several ways to achieve this:

1. Look at the data. Are they in the best shape for the algorithm? Regarding NaN, Covariance and so on? Are they normalized, are the categorical ones translated well? This is a question too far-reaching for a forum.

2. Look at the problem and the different algorithm suitable for this problem. Maybe

• Logistic Regression
• SVN
• XGBoost
• ....

1. Try hyper parameter tuning with RandomisedsearvCV or GridSearchCV

This is quite high-level.

This isn't particularly elegant, but it works:

When something like this happens, it's usually due to bad ordering of initialization.

The initialization of the Player class goes this way:

1. the name property has its backing field initialized with the _name value
2. the init block is run, and tries to access name
3. the getter of name tries to read the hometown property, but fails because hometown is still not initialized
4. ...if things had gone right, the hometown property would be initialized now with the lazy delegate

So basically you're trying to access hometown before the lazy delegate is configured. If you move hometown's declaration above the init block, you should be fine.

You can see the fix in action on the playground

This could be because select_from_model refits the estimator by default and sklearn.ensembe.RandomForestClassifier has two pseudo random parameters: bootsrap, which is set to True by default, and max_features, which is set to 'auto' by default.

If you did not set a random_state in your randomforestclassifier estimator, then it will most likely yield different results every time you fit the model because of the randomness introduced by the bootstrap and max_features parameters, even on the same training data.

• bootstrap=True means that each tree will be trained on a random sample (with replacement) of a certain percentage of the observations from the training dataset.

• max_features='auto' means that when building each node, only the square root of the number of features in your training data will be considered to pick the cutoff point that reduces the gini impurity most.

You can do two things to ensure you get the same results:

1. Train your estimator first and then use select_from_model(randomforestclassifier, refit=False).
2. Declare randomforestclassifier with a random seed and then use select_from_model.

Needless to say, both options require you to pass the same X and y data.

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.