14-Model-Evaluation-Confusion-matrix.ipynb

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   "source": [
    "# Model Evaluation, Scoring Metrics, and Dealing with Imbalanced Classes"
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    "In the previous notebook, we already went into some detail on how to evaluate a model and how to pick the best model. So far, we assumed that we were given a performance measure, a measure of the quality of the model. What measure one should use is not always obvious, though. The default scores in scikit-learn are accuracy for classification, which is the fraction of correctly classified samples, and r2 for regression, with is the coefficient of determination.\n",
    "\n",
    "These are reasonable default choices in many scenarious; however, depending on our task, these are not always the definitive or recommended choices.\n",
    "\n",
    "Let's take look at classification in more detail, going back to the application of classifying handwritten digits. So, how about training a classifier and walking through the different ways we can evaluate it? Scikit-learn has many helpful methods in the `sklearn.metrics` module that can help us with this task:"
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    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "np.set_printoptions(precision=2)"
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   "cell_type": "code",
   "execution_count": null,
   "id": "67de0cdb",
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