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How to stack classifiers for higher predictive performance?
Materials and methods: Using Scikit-learn, we generate a Madelon-like data set for a classification task. Then a Support Vector classifier (SVC), Nu-Support Vector classifier (NuSVC), a Multi-layer perceptron (MLP), and a Random Forest classifier will be individually trained.
How does filtering affect accuracy of a classifier?
Filtering and feature selection can have large impact on classifier accuracy and area under the curve (AUC), as noisy data can confuse classifier and lead it to catch wrong patterns in training data. The common approach in data filtering is using proximity graphs.
How to use meta classifier to make predictions?
Use the first half of your training data to train the level one classifiers. Then use the trained level one classifiers to make predictions on the second half of the training data. These predictions should then be used to train meta-classifier. A more robust way to do this, is to use k-fold cross validation to generate the level one predictions.
How is classifier optimized for precision in scikit-learn?
That classifier was optimized for precision. For comparison, to show how GridSearchCV selects the best classifier, the function call below returns a classifier optimized for recall. The grid might be similar to the grid above, the only difference is that the classifer with the highest recall will be refit.
How are classifiers used to generate level one predictions?
A more robust way to do this, is to use k-fold cross validation to generate the level one predictions. Here, the training data is split into k-folds. Then the first k-1 folds are used to train the level one classifiers. The validation fold is then used to generate a subset of the level one predictions.
How are classifiers trained in stacking classifier framework?
The meta-classifier can be any classifier of your choice. Figure 1 shows how three different classifiers get trained. Their predictions get stacked and are used as features to train the meta-classifier which makes the final prediction. Figure 1 — Schematic of a stacking classifier framework.
How is the performance of a classifier measured?
The performance of each classifier will be measured using the area under the receiver operating curve (AUC). Finally, we stack the predictions of these classifiers using the StackingCVClassifier object and compare the results.