concepts/regression_error_metrics.ipynb

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    "# Regression Error Metrics\n",
    "Regression is a task when a model attempts to predict continuous values (not categorical values - which is classification)\n",
    "\n",
    "Examples of Evaluating Regression:\n",
    "1. Attempting to predict the price of a house given its features.\n",
    "    - (predicting the country a house is in is classification)\n",
    "    \n",
    "Common Evaluation Metrics:\n",
    "\n",
    "### Mean Absolute Error (MAE)\n",
    "\n",
    "\n",
    "The mean of the absolute value of errors. For example:\n",
    "- Compare the prediction of the house price to the true house price.\n",
    "\n",
    "Take the difference between true price minus the predicted price & take the absolute value.\n",
    "\n",
    "\n",
    "\n",
    "$$\n",
    "\\text{MAE} = \\frac{1}{n} \\sum_{i=1}^{n} | y_i - \\hat{y}_i |\n",
    "$$\n",
    "\n",
    "real number denoted by `||` & inside that is true minus predicted \n",
    "\n"
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    "### Mean Squared Error (MSE)\n",
    "\n",
    "$$\n",
    "\\text{MSE} = \\frac{1}{n} \\sum_{i=1}^{n} (y_i - \\hat{y}_i)^2\n",
    "$$\n",
    "\n",
    "\n"
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    "\n",
    "### Root Mean Square Error (RMSE)\n",
    "\n",
    "$$\n",
    "\\text{RMSE} = \\sqrt{\\frac{1}{n} \\sum_{i=1}^{n} (y_i - \\hat{y}_i)^2}\n",
    "$$"
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