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footprint/usage.\n\n\nIntroduction\n============\n\nEnsemble models can be very memory-intensive sometimes, for example, depending on the number of estimators and its depths, if you think of a tree-based ensemble model. 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It trades performance for reduced memory usage.\n\n\nInstallation\n============\n\n```\n$ pip install scikit-small-ensemble\n```\n\n\nUsage\n=====\n\n\n```python\n\n# random forest ensemble model\nfrom scikit_small_ensemble import compress, memory_map\n\n# WARNING: This changes the model object itself.\n# ratio is [0.0, 1.0], where 1.0 is most compressed.\ncompress(model, ratio=0.2)\n\n# Or, you can memory-map estimators from the disk on demand.\n# memory_map(model, ratio=1.0)\n\n# Use it like nothing happened.\n# Memory usage becomes 10x lower with ratio=1.0\nY = model.predict_proba(X)\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fstewartpark%2Fscikit-small-ensemble","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fstewartpark%2Fscikit-small-ensemble","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fstewartpark%2Fscikit-small-ensemble/lists"}