How do you select important variables in machine learning?

How do you select important variables in machine learning?

Feature Selection: Select a subset of input features from the dataset.

1. Unsupervised: Do not use the target variable (e.g. remove redundant variables). Correlation.
2. Supervised: Use the target variable (e.g. remove irrelevant variables). Wrapper: Search for well-performing subsets of features. RFE.

What are several learning strategies in machine learning?

The basic strategies for machine learning in general are (1) supervised learning, (2) unsupervised learning, and (3) reinforcement learning. In supervised learning, the training set consists of data that have been labeled and annotated by a human observer.

How to calculate feature importance in machine learning?

So, we need an alternative technique that will be able to calculate the feature importances on a test dataset, and also calculate it based on different metrics such as accuracy, precision or recall.

How does permutation feature importance improve machine learning?

The permutation feature importance technique overcomes the drawbacks that we saw above, with a very simple concept: how does the model perform if the only the values of a feature are randomly shuffled? In other words, if we make a feature unusable for the model, what is the impact on the performance?

Why are precision and recall important in machine learning?

Clearly we have an algorithm that is overfitting. The accuracy on the test set is lower than the training set, but not too bad. Since we are dealing with an imbalanced dataset though, the precision and recall numbers are more important. And there is a huge difference in the precision and recall between the training and test sets.

How to find variable importance for any model?

Practical walkthroughs on machine learning, data exploration and finding insight. My colleague showed me a nifty concept to easily calculate variable importance for any model. He did not invent it, actually many forms of it abound in the literature and on the Internet. John Elder is one of the authors.