A very simple script in which we decide whether it's better to use a model or an ensemble for making predictions by creating both (given an input source) and evaluating the results, choosing the one with best f-1 measure in its evaluation if the objective field is categorical, or r-measure for regression problems.
Given an input dataset:
Create a dataset with the input source.
Split it into training and test parts (80%/20%).
Create a model using the training dataset.
Create an ensemble using the training dataset.
Evaluate both the model and the ensemble using the test dataset.
A very simple script in which we decide whether it's better to use a model or an ensemble for making predictions by creating both (given an input source) and evaluating the results, choosing the one with best f-1 measure in its evaluation if the objective field is categorical, or r-measure for regression problems.
Given an input dataset:
Create a dataset with the input source.
Split it into training and test parts (80%/20%).
Create a model using the training dataset.
Create an ensemble using the training dataset.
Evaluate both the model and the ensemble using the test dataset.
Compare their evaluations and choose the best.
A very simple script in which we decide whether it's better to use a model or an ensemble for making predictions by creating both (given an input source) and evaluating the results, choosing the one with best f-1 measure in its evaluation if the objective field is categorical, or r-measure for regression problems.
Given an input dataset:
Create a dataset with the input source.
Split it into training and test parts (80%/20%).
Create a model using the training dataset.
Create an ensemble using the training dataset.
Evaluate both the model and the ensemble using the test dataset.
