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        "author": "Rajwardhan Shinde",
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            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
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        "description": "# LGBtrainer helps you find hyper params for LGBM and simplifies the process of training the model and finding hyperparams.\n\n* Parameters:-\n\n1. train = it should be your train dataset(which is fit for training purpose)\n2. test = it should be your test dataset(which is fit for testing purpose)\n3. y_train = it should be your target column or values(same rows as train)\n4. cv = the number of splits or folds(it is used for both finding hyperparams + training the model)\n5. num_rounds = number of training rounds(it is used for both finding hyperparams + training the model)\n6. metric = only 'auc' and 'rmse' can be used(For now only these two are supported)\n7. objective = 'binary' or 'regression' or any other can be provided\n8. max_eval = number of evaluations performed for finding params(note:- larger number might take more time depending on size of dataset)\n\n* Example:-\n\n```\n-from LGBtrainer import Model\n-model = Model(train, test, y_train, metric='auc', objective='binary', max_eval=3, cv=5)\n-params = model.get_params()\n-predictions = model.lgb_model(params)\n```\n\n\n",
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        "name": "LGBtrainer",
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        "summary": "Find LGBM Hyperparams and train the model",
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