Question 1

How to install clj-ml in a Clojure project?

Accepted Answer

Add the dependency from Clojars using [cc.artifice/clj-ml "0.8.5"] in your project.clj or deps.edn file, as specified in the installation section. It's also available via Maven coordinates for integration with build tools like Leiningen.

Question 2

Clj-ml vs Incanter for Clojure data science?

Accepted Answer

clj-ml is focused on machine learning with Weka's algorithms, ideal for classification and regression tasks, while Incanter is broader for statistics and visualization. Choose clj-ml for direct ML model building, but Incanter for exploratory data analysis.

Question 3

How to handle missing values in datasets with clj-ml?

Accepted Answer

Use the 'replace-missing-values' filter, which automatically replaces missing numeric values with the mean and nominal values with the mode, as demonstrated in the Titanic example preprocessing steps.

Question 4

Can clj-ml do deep learning?

Accepted Answer

No, it only supports basic neural networks like multilayer perceptrons via Weka. For advanced deep learning, you'd need to look at other Clojure libraries that interface with frameworks like Deeplearning4j or use Java interop directly.

Question 5

How to save and load trained models in clj-ml?

Accepted Answer

Use serialize-to-file to save a classifier to disk and deserialize-from-file to reload it, as shown in the classifier persistence example. This allows reusing models without retraining, but ensure compatibility with Java serialization.

Question 6

Is clj-ml suitable for large datasets?

Accepted Answer

Since it's based on Weka, which is in-memory, it may struggle with very large datasets that exceed available RAM. Consider sampling or using distributed alternatives for big data applications.

clj-ml

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