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        "author": "Daniel Danis",
        "author_email": "daniel.gordon.danis@gmail.com",
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        "description": "# ddplt\n\nUseful utility functions for evaluation of ML.\n\n**Motivation:**\nThe main motivation behind this package is to create a single place where the utility functions for ML projects are located. These functions represent the best I was able to scrape from various tutorials or offical documentation on the web.\n\n\n## Confusion matrix\n\nThis function prints and plots the confusion matrix.\n\nThe code:\n\n```python\nimport numpy as np\nfrom ddplt import plot_confusion_matrix\n\ny_test = np.array([0, 0, 1, 1, 2, 0])\ny_pred = np.array([0, 1, 1, 2, 2, 0])\nclass_names = np.array(['hip', 'hop', 'pop'])\nax, cm = plot_confusion_matrix(y_test, y_pred, class_names)\n```\n\nwill create a plot like:\n![conf_matrix](img/cm_hip_hop_pop.png)\n\n\n## Learning curve\n\nCreate plot showing performance evaluation for different sizes of training data. The method should accept: \n- existing `Axes`\n- performance measure (e.g. accuracy, MSE, precision, recall, etc.)\n- ...\n\n\n## ROC curve\n\nPlot showing Receiver Operating Characteristics of a predictor.\n\n\n## Correlation heatmap\n\nGrid where each square has a color denoting strength of a correlation between predictors. You can choose between Pearson and Spearman correlation coefficient, the result is shown inside the square. \n\n\n\n",
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        "keywords": "plotting machine learning evaluation metrics",
        "license": "GPLv3",
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        "name": "ddplt",
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        "summary": "Useful utility functions for evaluation of ML",
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