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learning models on data. It reports scores, confusion matrices, predictions, and plots the scores ranked by classifier performance.\n\n## Installation\n```bash\npip install polyssifier\n```\n\n## How to use\n### For classification\n```python\nfrom polyssifier import poly\n# Load data\ndata = np.load(\"/path/to/data.npy\")\nlabel = np.load(\"/path/to/labels.npy\")\n# Run analysis\nreport = poly(data,label, n_folds=8)\n# Plot results\nreport.plot_scores()\nreport.plot_features(ntop=10)\n```\n\n### For Regression\n```python\nfrom polyssifier import polyr\n# Load data\ndata = np.load(\"/path/to/data.npy\")\ntarget = np.load(\"/path/to/target.npy\")\n# Run analysis\nreport = polyr(data, target, n_folds=8)\n# Plot results\nreport.plot_scores()\nreport.plot_features(ntop=10)\n```\n\n### In the terminal\n```bash\npoly data.npy label.npy --concurrency 10\n```\n\n### Requirements\n - Sklearn\n - Numpy\n - Pandas\n\n### Features\n - Cross validated scores.\n   - Report f1 score (scoring='f1') or ROC (scoring='auc') for classification\n   - Report MSE or R^2 for regression\n - Feature ranking for compatible models (Logistic Regression, Linear SVM, Random Forest)\n - Parallel processing. \n   - Control the number of threads with 'concurrency'.\n   - We recommend setting concurrency to half the number of Cores in your system.\n - Saves trained models for future use in case of server malfunction. \n   - Set project_name for identifying a experiment.\n - Activate feature selection step setting \n   - feature_selection=True\n - Automatically scales your data with scale=True\n\nExample: on [sample/example.ipynb](sample/example.ipynb)\n\nIt includes the following classifiers:\n\n- Multilayer Perceptron\n- Nearest Neighbors\n- Linear SVM\n- RBF SVM\n- Decision Tree\n- Random Forest\n- Logistic Regression\n- Naive Bayes\n- Voting Classifier\n\nand the following regressors:\n\n- Linear Regression\n- Bayesian Ridge\n- PassiveAggressiveRegressor\n- GaussianProcessRegressor\n- Ridge\n- Lasso\n- Lars\n- LassoLars\n- OrthogonalMatchingPursuit\n- ElasticNet\n\nYou can exclude some of this models by providing a list of names as follows:\n```python\nfrom polyssifier import poly\n\nreport = poly(data,label, n_folds=8,\n              exclude=['Multilayer Perceptron'])\n```\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falvarouc%2Fpolyssifier","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Falvarouc%2Fpolyssifier","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falvarouc%2Fpolyssifier/lists"}