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        "description": "# CrossTrainer: Practical Domain Adaptation with Loss Reweighting\n\nThis is an implementation of the method described in \"CrossTrainer: Practical Domain Adaptation with Loss Reweighting\" by Justin Chen, Edward Gan, Kexin Rong, Sahaana Suri, and Peter Bailis.\n\n### Install\nThe crosstrainer package can be installed using pip.\n\n```\npip install crosstrainer\n```\n\n### Usage\n\nCrossTrainer utilizes loss reweighting to train machine learning models using data from a target task with supplementary source data.\n\n##### Inputs:\nBase model, target data, source data.\n\n##### Outputs:\nTrained model with optimized weighting parameter alpha.\n\n##### Example:\n\n```python\nimport crosstrainer\nfrom sklearn import linear_model\n\nlr = linear_model.LogisticRegression()\nct = CrossTrainer(lr, k=5, delta=0.01)\nlr, alpha = ct.fit(X_target, y_target, X_source, y_source)\ny_pred = lr.predict(X_test)\n```\n\nMore examples can be found in the tests file: ```crosstrainer/tests/test_crosstrainer.py```.\n\n",
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