Optimizing Hyperparameters the right Way

Optimizing Hyperparameters the right Way

In this post, we will build a machine learning pipeline using multiple optimizers and use the power of Bayesian Optimization to arrive at the most optimal configuration for all our parameters. All we need is the sklearn Pipeline and Skopt.

In this post, we will build a machine learning pipeline using multiple optimizers and use the power of Bayesian Optimization to arrive at the most optimal configuration for all our parameters. All we need is the sklearn Pipeline and Skopt.

You can use your favorite ML models, as long as they have a sklearn wrapper (looking at you XGBoost or NGBoost).

About Hyperparameters

The critical point for finding the best models that can solve a problem are not just the models. We need to** find the optimal parameters** to make our model work optimally, given the dataset. This is called finding or searching hyperparameters.

For example, we would like to implement a Random Forest in practice and its documentation states:

class sklearn.ensemble.RandomForestClassifier(n_estimators=100, *, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=None, ...

All of those parameters can be explored. This can include all possible number of estimators ( n_estimators ) in the forest from 1 to 10.000, you may try to split using {“gini”, “entropy”}, or the maximum depth of your trees is another integer and there are many, many more options. Each of those parameters can influence your model‘s performance and worst of all most of the time you do not know the right configuration when you’re starting out with a new problem-set.

machine-learning data-science hyperparameter-tuning scikit-learn optimization

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