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Update 05.03-Hyperparameters-and-Model-Validation.ipynb
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notebooks/05.03-Hyperparameters-and-Model-Validation.ipynb

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"![](figures/05.03-bias-variance-2.png)\n",
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"[figure source in Appendix](06.00-Figure-Code.ipynb#Bias-Variance-Tradeoff-Metrics)\n",
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"\n",
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"The score here is the $R^2$ score, or [coefficient of determineation](https://en.wikipedia.org/wiki/Coefficient_of_determination), which measures how well a model performs relative to a simple mean of the target values. $R^2=1$ indicates a perfect match, $R^2=0$ indicates the model does no better than simply taking the mean of the data, and negative values mean even worse models.\n",
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"The score here is the $R^2$ score, or [coefficient of determination](https://en.wikipedia.org/wiki/Coefficient_of_determination), which measures how well a model performs relative to a simple mean of the target values. $R^2=1$ indicates a perfect match, $R^2=0$ indicates the model does no better than simply taking the mean of the data, and negative values mean even worse models.\n",
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"From the scores associated with these two models, we can make an observation that holds more generally:\n",
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"\n",
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"- For high-bias models, the performance of the model on the validation set is similar to the performance on the training set.\n",

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