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).

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.

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

Getting Started with scikit-learn Pipelines for Machine Learning: Building a pipeline from the ground up. (All code in this post is also included in this GitHub repository.)

Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant

Learning is a new fun in the field of Machine Learning and Data Science. In this article, we’ll be discussing 15 machine learning and data science projects.

Hyperparameter optimization refers to performing a search in order to discover the set of specific model configuration arguments that result in the best performance of the model on a specific dataset.

This post will help you in finding different websites where you can easily get free Datasets to practice and develop projects in Data Science and Machine Learning.